5857–5872 di 82931 risultati

Heaven Painted as a Cop Car

Eve Bryson died and found herself dead and lusting after a cop?
And not just any cop. Deputy McCall Cascade: Superhero.
Can a ghost and a superhero find true happiness and sex and stop a few killers along the way?
USA Today bestselling author Dean Wesley Smith brings you the fourth book in the crazy Ghost of a Chance series.
“[The Poker Boy] series is unlike anything else out there. It’s quirky and a lot of fun.”
\- Amazing Stories

Eve Bryson died and found herself dead and lusting after a cop?
And not just any cop. Deputy McCall Cascade: Superhero.
Can a ghost and a superhero find true happiness and sex and stop a few killers along the way?
USA Today bestselling author Dean Wesley Smith brings you the fourth book in the crazy Ghost of a Chance series.
“[The Poker Boy] series is unlike anything else out there. It’s quirky and a lot of fun.”
\- Amazing Stories

Only registered users can download this free product.

Heaven Painted as a Christmas Gift

Poker Boy and his team have saved the world countless times. The Ghost of a Chance agency follows a similar charge. Superheroes and ghosts, all working for the greater good.
But as the holidays approach, both groups face a challenge with higher stakes than either can tackle alone.
The Ghost Agents, including newly dead recruits Belle and Nancy, team up with Poker Boy and his team to stop a deadly threat—and save Christmas.
Melding his fan-favorite Poker Boy series with his new Heaven Painted series, USA Today bestselling author Dean Wesley Smith proves that alive or dead, god or mortal, no one has more fun saving the world.
“[The Poker Boy] series is unlike anything else
out there. It’s quirky and a lot of fun.”
—Amazing Stories
USA Today bestselling author Dean Wesley Smith published more than a hundred novels in thirty years and hundreds and hundreds of short stories across many genres.
He wrote a couple dozen Star Trek novels, the only two original Men in Black novels, Spider-Man and X-Men novels, plus novels set in gaming and television worlds. Writing with his wife Kristine Kathryn Rusch under the name Kathryn Wesley, they wrote the novel for the NBC miniseries The Tenth Kingdom and other books for Hallmark Hall of Fame movies.
He wrote novels under dozens of pen names in the worlds of comic books and movies, including novelizations of a dozen films, from The Final Fantasy to Steel to Rundown.
He now writes his own original fiction under just the one name, Dean Wesley Smith. In addition to his upcoming novel releases, his monthly magazine called Smith’s Monthly premiered October 1, 2013, filled entirely with his original novels and stories.
Dean also worked as an editor and publisher, first at Pulphouse Publishing, then for VB Tech Journal, then for Pocket Books. He now plays a role as an executive editor for the original anthology series Fiction River.

Poker Boy and his team have saved the world countless times. The Ghost of a Chance agency follows a similar charge. Superheroes and ghosts, all working for the greater good.
But as the holidays approach, both groups face a challenge with higher stakes than either can tackle alone.
The Ghost Agents, including newly dead recruits Belle and Nancy, team up with Poker Boy and his team to stop a deadly threat—and save Christmas.
Melding his fan-favorite Poker Boy series with his new Heaven Painted series, USA Today bestselling author Dean Wesley Smith proves that alive or dead, god or mortal, no one has more fun saving the world.
“[The Poker Boy] series is unlike anything else
out there. It’s quirky and a lot of fun.”
—Amazing Stories
USA Today bestselling author Dean Wesley Smith published more than a hundred novels in thirty years and hundreds and hundreds of short stories across many genres.
He wrote a couple dozen Star Trek novels, the only two original Men in Black novels, Spider-Man and X-Men novels, plus novels set in gaming and television worlds. Writing with his wife Kristine Kathryn Rusch under the name Kathryn Wesley, they wrote the novel for the NBC miniseries The Tenth Kingdom and other books for Hallmark Hall of Fame movies.
He wrote novels under dozens of pen names in the worlds of comic books and movies, including novelizations of a dozen films, from The Final Fantasy to Steel to Rundown.
He now writes his own original fiction under just the one name, Dean Wesley Smith. In addition to his upcoming novel releases, his monthly magazine called Smith’s Monthly premiered October 1, 2013, filled entirely with his original novels and stories.
Dean also worked as an editor and publisher, first at Pulphouse Publishing, then for VB Tech Journal, then for Pocket Books. He now plays a role as an executive editor for the original anthology series Fiction River.

Only registered users can download this free product.

Heart of Flame

Former homicide detective Chuck Montiel didn’t believe in vampires until the night he was turned. Now he’s a deadly and powerful predator, struggling to hold onto his humanity, and his only chance at salvation is an offer from Branch Zero – a secret organization dedicated to keeping L.A.’s preternatural populace in check. Chuck can either help them track down a dangerous cult threatening the city or spend the rest of eternity under Branch Zero’s surveillance. He’s got no hope, no options, and nothing to lose. Until Misha dances into his bleak existence. A half-seraph with the face of an angel and hands full of flame, Misha runs the Wayward Heart Hotel, a sanctuary for L.A.’s magical underworld. Branch Zero don’t approve of her or her methods. She and Chuck are on opposite sides of the law, but their connection is shocking and undeniable – they just have to survive long enough to understand it.

Former homicide detective Chuck Montiel didn’t believe in vampires until the night he was turned. Now he’s a deadly and powerful predator, struggling to hold onto his humanity, and his only chance at salvation is an offer from Branch Zero – a secret organization dedicated to keeping L.A.’s preternatural populace in check. Chuck can either help them track down a dangerous cult threatening the city or spend the rest of eternity under Branch Zero’s surveillance. He’s got no hope, no options, and nothing to lose. Until Misha dances into his bleak existence. A half-seraph with the face of an angel and hands full of flame, Misha runs the Wayward Heart Hotel, a sanctuary for L.A.’s magical underworld. Branch Zero don’t approve of her or her methods. She and Chuck are on opposite sides of the law, but their connection is shocking and undeniable – they just have to survive long enough to understand it.

Only registered users can download this free product.

Hawthorn Witches: Demons & Dracaena, Sorcerers & Sumac, Werewolves & Wisteria

This omnibus edition of the Hawthorn Witches series includes the first 3 novellas in the series:
Demons & Dracaena, Hawthorn Witches Novella #1
Sorcerers & Sumac, Hawthorn Witches Novella #2
Werewolves & Wisteria, Hawthorn Witches Novella #3
Annie Hawthorn’s life is a living hell. She’s about to graduate from high school in beautiful Bellmoral, Colorado, but she’s caught the eye of the campus mean girl. In order to get back at her, Annie unleashes a little hell of her own when she discovers her late aunt’s grimoire.
Now that there’s a demon on the loose, things aren’t so funny. Annie’s best friend has been turned into a cat. She’s doing her homework in hell. Her after school job at the greenhouse is punctuated by frequent demonic interruptions.
But it’s worse than that, because Charlie, the demon, claims that her aunt isn’t dead, and she owes him a debt. And until Annie can find her, he’s holding her life ransom. **
### Sinossi
This omnibus edition of the Hawthorn Witches series includes the first 3 novellas in the series:
Demons & Dracaena, Hawthorn Witches Novella #1
Sorcerers & Sumac, Hawthorn Witches Novella #2
Werewolves & Wisteria, Hawthorn Witches Novella #3
Annie Hawthorn’s life is a living hell. She’s about to graduate from high school in beautiful Bellmoral, Colorado, but she’s caught the eye of the campus mean girl. In order to get back at her, Annie unleashes a little hell of her own when she discovers her late aunt’s grimoire.
Now that there’s a demon on the loose, things aren’t so funny. Annie’s best friend has been turned into a cat. She’s doing her homework in hell. Her after school job at the greenhouse is punctuated by frequent demonic interruptions.
But it’s worse than that, because Charlie, the demon, claims that her aunt isn’t dead, and she owes him a debt. And until Annie can find her, he’s holding her life ransom.

This omnibus edition of the Hawthorn Witches series includes the first 3 novellas in the series:
Demons & Dracaena, Hawthorn Witches Novella #1
Sorcerers & Sumac, Hawthorn Witches Novella #2
Werewolves & Wisteria, Hawthorn Witches Novella #3
Annie Hawthorn’s life is a living hell. She’s about to graduate from high school in beautiful Bellmoral, Colorado, but she’s caught the eye of the campus mean girl. In order to get back at her, Annie unleashes a little hell of her own when she discovers her late aunt’s grimoire.
Now that there’s a demon on the loose, things aren’t so funny. Annie’s best friend has been turned into a cat. She’s doing her homework in hell. Her after school job at the greenhouse is punctuated by frequent demonic interruptions.
But it’s worse than that, because Charlie, the demon, claims that her aunt isn’t dead, and she owes him a debt. And until Annie can find her, he’s holding her life ransom. **
### Sinossi
This omnibus edition of the Hawthorn Witches series includes the first 3 novellas in the series:
Demons & Dracaena, Hawthorn Witches Novella #1
Sorcerers & Sumac, Hawthorn Witches Novella #2
Werewolves & Wisteria, Hawthorn Witches Novella #3
Annie Hawthorn’s life is a living hell. She’s about to graduate from high school in beautiful Bellmoral, Colorado, but she’s caught the eye of the campus mean girl. In order to get back at her, Annie unleashes a little hell of her own when she discovers her late aunt’s grimoire.
Now that there’s a demon on the loose, things aren’t so funny. Annie’s best friend has been turned into a cat. She’s doing her homework in hell. Her after school job at the greenhouse is punctuated by frequent demonic interruptions.
But it’s worse than that, because Charlie, the demon, claims that her aunt isn’t dead, and she owes him a debt. And until Annie can find her, he’s holding her life ransom.

Only registered users can download this free product.

Hands-On Unsupervised Learning With Python

**Discover the skill-sets required to implement various approaches to Machine Learning with Python**
#### Key Features
* Explore unsupervised learning with clustering, autoencoders, restricted Boltzmann machines, and more
* Build your own neural network models using modern Python libraries
* Practical examples show you how to implement different machine learning and deep learning techniques

#### Book Description
Unsupervised learning is about making use of raw, untagged data and applying learning algorithms to it to help a machine predict its outcome. With this book, you will explore the concept of unsupervised learning to cluster large sets of data and analyze them repeatedly until the desired outcome is found using Python.
This book starts with the key differences between supervised, unsupervised, and semi-supervised learning. You will be introduced to the best-used libraries and frameworks from the Python ecosystem and address unsupervised learning in both the machine learning and deep learning domains. You will explore various algorithms, techniques that are used to implement unsupervised learning in real-world use cases. You will learn a variety of unsupervised learning approaches, including randomized optimization, clustering, feature selection and transformation, and information theory. You will get hands-on experience with how neural networks can be employed in unsupervised scenarios. You will also explore the steps involved in building and training a GAN in order to process images.
By the end of this book, you will have learned the art of unsupervised learning for different real-world challenges.
#### What you will learn
* Use cluster algorithms to identify and optimize natural groups of data
* Explore advanced non-linear and hierarchical clustering in action
* Soft label assignments for fuzzy c-means and Gaussian mixture models
* Detect anomalies through density estimation
* Perform principal component analysis using neural network models
* Create unsupervised models using GANs

#### Who this book is for
This book is intended for statisticians, data scientists, machine learning developers, and deep learning practitioners who want to build smart applications by implementing key building block unsupervised learning, and master all the new techniques and algorithms offered in machine learning and deep learning using real-world examples. Some prior knowledge of machine learning concepts and statistics is desirable.

**Discover the skill-sets required to implement various approaches to Machine Learning with Python**
#### Key Features
* Explore unsupervised learning with clustering, autoencoders, restricted Boltzmann machines, and more
* Build your own neural network models using modern Python libraries
* Practical examples show you how to implement different machine learning and deep learning techniques

#### Book Description
Unsupervised learning is about making use of raw, untagged data and applying learning algorithms to it to help a machine predict its outcome. With this book, you will explore the concept of unsupervised learning to cluster large sets of data and analyze them repeatedly until the desired outcome is found using Python.
This book starts with the key differences between supervised, unsupervised, and semi-supervised learning. You will be introduced to the best-used libraries and frameworks from the Python ecosystem and address unsupervised learning in both the machine learning and deep learning domains. You will explore various algorithms, techniques that are used to implement unsupervised learning in real-world use cases. You will learn a variety of unsupervised learning approaches, including randomized optimization, clustering, feature selection and transformation, and information theory. You will get hands-on experience with how neural networks can be employed in unsupervised scenarios. You will also explore the steps involved in building and training a GAN in order to process images.
By the end of this book, you will have learned the art of unsupervised learning for different real-world challenges.
#### What you will learn
* Use cluster algorithms to identify and optimize natural groups of data
* Explore advanced non-linear and hierarchical clustering in action
* Soft label assignments for fuzzy c-means and Gaussian mixture models
* Detect anomalies through density estimation
* Perform principal component analysis using neural network models
* Create unsupervised models using GANs

#### Who this book is for
This book is intended for statisticians, data scientists, machine learning developers, and deep learning practitioners who want to build smart applications by implementing key building block unsupervised learning, and master all the new techniques and algorithms offered in machine learning and deep learning using real-world examples. Some prior knowledge of machine learning concepts and statistics is desirable.

Only registered users can download this free product.

Hands-On Serverless Computing

**Deploy functions efficiently using different cloud-based serverless offerings**
#### Key Features
* Understand the concept of Function-as-a-Service
* Implement Serverless solutions using AWS Lambda, Azure Functions and Google Cloud Functions
* Practical approach towards choosing the best tool for your serverless environment

#### Book Description
Serverless applications and architectures are gaining momentum and are increasingly being used by companies of all sizes. Serverless software takes care of many problems that developers face when running systems and servers, such as fault tolerance, centralized logging, horizontal scalability, and deployments.
You will learn how to harness serverless technology to rapidly reduce production time and minimize your costs, while still having the freedom to customize your code, without hindering functionality. Upon finishing the book, you will have the knowledge and resources to build your own serverless application hosted in AWS, Microsoft Azure, or Google Cloud Platform, and will have experienced the benefits of event-driven technology for yourself.
This hands-on guide dives into the basis of serverless architectures and how to build them using Node.js as a programming language, Visual Studio Code for code editing, and Postman for quickly and securely developing applications without the hassle of configuring and maintaining infrastructure on three public cloud platforms.
#### What you will learn
* Understand the benefts of serverless computing and know when to use it
* Develop serverless applications on AWS, Azure, and Google Cloud
* Get to grips with Function as a Service (FaaS)
* Apply triggers to serverless functions
* Build event-driven apps using serverless frameworks
* Use the Node.js programming language to build serverless apps
* Use code editors, such as Visual Studio Code, as development environments
* Master the best development practices for creating scalable and practical solutions

#### Who this book is for
This book is targeted towards developers, system administrators or any stakeholder working in the Serverless environment and want to understand how functions work.
Basic idea of serverless architecture can be an added advantage

**Deploy functions efficiently using different cloud-based serverless offerings**
#### Key Features
* Understand the concept of Function-as-a-Service
* Implement Serverless solutions using AWS Lambda, Azure Functions and Google Cloud Functions
* Practical approach towards choosing the best tool for your serverless environment

#### Book Description
Serverless applications and architectures are gaining momentum and are increasingly being used by companies of all sizes. Serverless software takes care of many problems that developers face when running systems and servers, such as fault tolerance, centralized logging, horizontal scalability, and deployments.
You will learn how to harness serverless technology to rapidly reduce production time and minimize your costs, while still having the freedom to customize your code, without hindering functionality. Upon finishing the book, you will have the knowledge and resources to build your own serverless application hosted in AWS, Microsoft Azure, or Google Cloud Platform, and will have experienced the benefits of event-driven technology for yourself.
This hands-on guide dives into the basis of serverless architectures and how to build them using Node.js as a programming language, Visual Studio Code for code editing, and Postman for quickly and securely developing applications without the hassle of configuring and maintaining infrastructure on three public cloud platforms.
#### What you will learn
* Understand the benefts of serverless computing and know when to use it
* Develop serverless applications on AWS, Azure, and Google Cloud
* Get to grips with Function as a Service (FaaS)
* Apply triggers to serverless functions
* Build event-driven apps using serverless frameworks
* Use the Node.js programming language to build serverless apps
* Use code editors, such as Visual Studio Code, as development environments
* Master the best development practices for creating scalable and practical solutions

#### Who this book is for
This book is targeted towards developers, system administrators or any stakeholder working in the Serverless environment and want to understand how functions work.
Basic idea of serverless architecture can be an added advantage

Only registered users can download this free product.

Hands-On Reinforcement Learning with Python: Master reinforcement and deep reinforcement learning using OpenAI Gym and TensorFlow

**A hands-on guide enriched with examples to master deep reinforcement learning algorithms with Python**
#### Key Features
* Your entry point into the world of artificial intelligence using the power of Python
* An example-rich guide to master various RL and DRL algorithms
* Explore various state-of-the-art architectures along with math

#### Book Description
Reinforcement Learning (RL) is the trending and most promising branch of artificial intelligence. Hands-On Reinforcement learning with Python will help you master not only the basic reinforcement learning algorithms but also the advanced deep reinforcement learning algorithms. The book starts with an introduction to Reinforcement Learning followed by OpenAI Gym, and TensorFlow. You will then explore various RL algorithms and concepts, such as Markov Decision Process, Monte Carlo methods, and dynamic programming, including value and policy iteration. This example-rich guide will introduce you to deep reinforcement learning algorithms, such as Dueling DQN, DRQN, A3C, PPO, and TRPO. You will also learn about imagination-augmented agents, learning from human preference, DQfD, HER, and many more of the recent advancements in reinforcement learning. By the end of the book, you will have all the knowledge and experience needed to implement reinforcement learning and deep reinforcement learning in your projects, and you will be all set to enter the world of artificial intelligence.
#### What you will learn
* Understand the basics of reinforcement learning methods, algorithms, and elements
* Train an agent to walk using OpenAI Gym and Tensorflow
* Understand the Markov Decision Process, Bellman’s optimality, and TD learning
* Solve multi-armed-bandit problems using various algorithms
* Master deep learning algorithms, such as RNN, LSTM, and CNN with applications
* Build intelligent agents using the DRQN algorithm to play the Doom game
* Teach agents to play the Lunar Lander game using DDPG
* Train an agent to win a car racing game using dueling DQN

#### Who this book is for
If you’re a machine learning developer or deep learning enthusiast interested in artificial intelligence and want to learn about reinforcement learning from scratch, this book is for you. Some knowledge of linear algebra, calculus, and the Python programming language will help you understand the concepts covered in this book.
#### Table of Contents
1. Introduction to Reinforcement Learning
2. Getting started with OpenAI and Tensorflow
3. Markov Decision process and Dynamic Programming
4. Gaming with Monte Carlo Tree Search
5. Temporal Difference Learning
6. Multi-Armed Bandit Problem
7. Deep Learning Fundamentals
8. Deep Learning and Reinforcement
9. Playing Doom With Deep Recurrent Q Network
10. Asynchronous Advantage Actor Critic Network
11. Policy Gradients and Optimization
12. Capstone Project – Car Racing using DQN
13. Current Research and Next Steps

**
### Sinossi
**A hands-on guide enriched with examples to master deep reinforcement learning algorithms with Python**
#### Key Features
* Your entry point into the world of artificial intelligence using the power of Python
* An example-rich guide to master various RL and DRL algorithms
* Explore various state-of-the-art architectures along with math

#### Book Description
Reinforcement Learning (RL) is the trending and most promising branch of artificial intelligence. Hands-On Reinforcement learning with Python will help you master not only the basic reinforcement learning algorithms but also the advanced deep reinforcement learning algorithms. The book starts with an introduction to Reinforcement Learning followed by OpenAI Gym, and TensorFlow. You will then explore various RL algorithms and concepts, such as Markov Decision Process, Monte Carlo methods, and dynamic programming, including value and policy iteration. This example-rich guide will introduce you to deep reinforcement learning algorithms, such as Dueling DQN, DRQN, A3C, PPO, and TRPO. You will also learn about imagination-augmented agents, learning from human preference, DQfD, HER, and many more of the recent advancements in reinforcement learning. By the end of the book, you will have all the knowledge and experience needed to implement reinforcement learning and deep reinforcement learning in your projects, and you will be all set to enter the world of artificial intelligence.
#### What you will learn
* Understand the basics of reinforcement learning methods, algorithms, and elements
* Train an agent to walk using OpenAI Gym and Tensorflow
* Understand the Markov Decision Process, Bellman’s optimality, and TD learning
* Solve multi-armed-bandit problems using various algorithms
* Master deep learning algorithms, such as RNN, LSTM, and CNN with applications
* Build intelligent agents using the DRQN algorithm to play the Doom game
* Teach agents to play the Lunar Lander game using DDPG
* Train an agent to win a car racing game using dueling DQN

#### Who this book is for
If you’re a machine learning developer or deep learning enthusiast interested in artificial intelligence and want to learn about reinforcement learning from scratch, this book is for you. Some knowledge of linear algebra, calculus, and the Python programming language will help you understand the concepts covered in this book.
#### Table of Contents
1. Introduction to Reinforcement Learning
2. Getting started with OpenAI and Tensorflow
3. Markov Decision process and Dynamic Programming
4. Gaming with Monte Carlo Tree Search
5. Temporal Difference Learning
6. Multi-Armed Bandit Problem
7. Deep Learning Fundamentals
8. Deep Learning and Reinforcement
9. Playing Doom With Deep Recurrent Q Network
10. Asynchronous Advantage Actor Critic Network
11. Policy Gradients and Optimization
12. Capstone Project – Car Racing using DQN
13. Current Research and Next Steps

### L’autore
**Sudharsan Ravichandiran** is a data scientist, researcher, artificial intelligence enthusiast, and YouTuber (search for Sudharsan reinforcement learning). He completed his bachelors in information technology at Anna University. His area of research focuses on practical implementations of deep learning and reinforcement learning, which includes natural language processing and computer vision. He used to be a freelance web developer and designer and has designed award-winning websites. He is an open source contributor and loves answering questions on Stack Overflow.

**A hands-on guide enriched with examples to master deep reinforcement learning algorithms with Python**
#### Key Features
* Your entry point into the world of artificial intelligence using the power of Python
* An example-rich guide to master various RL and DRL algorithms
* Explore various state-of-the-art architectures along with math

#### Book Description
Reinforcement Learning (RL) is the trending and most promising branch of artificial intelligence. Hands-On Reinforcement learning with Python will help you master not only the basic reinforcement learning algorithms but also the advanced deep reinforcement learning algorithms. The book starts with an introduction to Reinforcement Learning followed by OpenAI Gym, and TensorFlow. You will then explore various RL algorithms and concepts, such as Markov Decision Process, Monte Carlo methods, and dynamic programming, including value and policy iteration. This example-rich guide will introduce you to deep reinforcement learning algorithms, such as Dueling DQN, DRQN, A3C, PPO, and TRPO. You will also learn about imagination-augmented agents, learning from human preference, DQfD, HER, and many more of the recent advancements in reinforcement learning. By the end of the book, you will have all the knowledge and experience needed to implement reinforcement learning and deep reinforcement learning in your projects, and you will be all set to enter the world of artificial intelligence.
#### What you will learn
* Understand the basics of reinforcement learning methods, algorithms, and elements
* Train an agent to walk using OpenAI Gym and Tensorflow
* Understand the Markov Decision Process, Bellman’s optimality, and TD learning
* Solve multi-armed-bandit problems using various algorithms
* Master deep learning algorithms, such as RNN, LSTM, and CNN with applications
* Build intelligent agents using the DRQN algorithm to play the Doom game
* Teach agents to play the Lunar Lander game using DDPG
* Train an agent to win a car racing game using dueling DQN

#### Who this book is for
If you’re a machine learning developer or deep learning enthusiast interested in artificial intelligence and want to learn about reinforcement learning from scratch, this book is for you. Some knowledge of linear algebra, calculus, and the Python programming language will help you understand the concepts covered in this book.
#### Table of Contents
1. Introduction to Reinforcement Learning
2. Getting started with OpenAI and Tensorflow
3. Markov Decision process and Dynamic Programming
4. Gaming with Monte Carlo Tree Search
5. Temporal Difference Learning
6. Multi-Armed Bandit Problem
7. Deep Learning Fundamentals
8. Deep Learning and Reinforcement
9. Playing Doom With Deep Recurrent Q Network
10. Asynchronous Advantage Actor Critic Network
11. Policy Gradients and Optimization
12. Capstone Project – Car Racing using DQN
13. Current Research and Next Steps

**
### Sinossi
**A hands-on guide enriched with examples to master deep reinforcement learning algorithms with Python**
#### Key Features
* Your entry point into the world of artificial intelligence using the power of Python
* An example-rich guide to master various RL and DRL algorithms
* Explore various state-of-the-art architectures along with math

#### Book Description
Reinforcement Learning (RL) is the trending and most promising branch of artificial intelligence. Hands-On Reinforcement learning with Python will help you master not only the basic reinforcement learning algorithms but also the advanced deep reinforcement learning algorithms. The book starts with an introduction to Reinforcement Learning followed by OpenAI Gym, and TensorFlow. You will then explore various RL algorithms and concepts, such as Markov Decision Process, Monte Carlo methods, and dynamic programming, including value and policy iteration. This example-rich guide will introduce you to deep reinforcement learning algorithms, such as Dueling DQN, DRQN, A3C, PPO, and TRPO. You will also learn about imagination-augmented agents, learning from human preference, DQfD, HER, and many more of the recent advancements in reinforcement learning. By the end of the book, you will have all the knowledge and experience needed to implement reinforcement learning and deep reinforcement learning in your projects, and you will be all set to enter the world of artificial intelligence.
#### What you will learn
* Understand the basics of reinforcement learning methods, algorithms, and elements
* Train an agent to walk using OpenAI Gym and Tensorflow
* Understand the Markov Decision Process, Bellman’s optimality, and TD learning
* Solve multi-armed-bandit problems using various algorithms
* Master deep learning algorithms, such as RNN, LSTM, and CNN with applications
* Build intelligent agents using the DRQN algorithm to play the Doom game
* Teach agents to play the Lunar Lander game using DDPG
* Train an agent to win a car racing game using dueling DQN

#### Who this book is for
If you’re a machine learning developer or deep learning enthusiast interested in artificial intelligence and want to learn about reinforcement learning from scratch, this book is for you. Some knowledge of linear algebra, calculus, and the Python programming language will help you understand the concepts covered in this book.
#### Table of Contents
1. Introduction to Reinforcement Learning
2. Getting started with OpenAI and Tensorflow
3. Markov Decision process and Dynamic Programming
4. Gaming with Monte Carlo Tree Search
5. Temporal Difference Learning
6. Multi-Armed Bandit Problem
7. Deep Learning Fundamentals
8. Deep Learning and Reinforcement
9. Playing Doom With Deep Recurrent Q Network
10. Asynchronous Advantage Actor Critic Network
11. Policy Gradients and Optimization
12. Capstone Project – Car Racing using DQN
13. Current Research and Next Steps

### L’autore
**Sudharsan Ravichandiran** is a data scientist, researcher, artificial intelligence enthusiast, and YouTuber (search for Sudharsan reinforcement learning). He completed his bachelors in information technology at Anna University. His area of research focuses on practical implementations of deep learning and reinforcement learning, which includes natural language processing and computer vision. He used to be a freelance web developer and designer and has designed award-winning websites. He is an open source contributor and loves answering questions on Stack Overflow.

Only registered users can download this free product.

Hands-On Intelligent Agents with OpenAI Gym: Your guide to developing AI agents using deep reinforcement learning

**Implement intelligent agents using PyTorch to solve classic AI problems, play console games like Atari, and perform tasks such as autonomous driving using the CARLA driving simulator**
#### Key Features
* Explore the OpenAI Gym toolkit and interface to use over 700 learning tasks
* Implement agents to solve simple to complex AI problems
* Study learning environments and discover how to create your own

#### Book Description
Many real-world problems can be broken down into tasks that require a series of decisions to be made or actions to be taken. The ability to solve such tasks without a machine being programmed requires a machine to be artificially intelligent and capable of learning to adapt. This book is an easy-to-follow guide to implementing learning algorithms for machine software agents in order to solve discrete or continuous sequential decision making and control tasks. Hands-On Intelligent Agents with OpenAI Gym takes you through the process of building intelligent agent algorithms using deep reinforcement learning starting from the implementation of the building blocks for configuring, training, logging, visualizing, testing, and monitoring the agent. You will walk through the process of building intelligent agents from scratch to perform a variety of tasks. In the closing chapters, the book provides an overview of the latest learning environments and learning algorithms, along with pointers to more resources that will help you take your deep reinforcement learning skills to the next level.
#### What you will learn
* Explore intelligent agents and learning environments
* Understand the basics of RL and deep RL
* Get started with OpenAI Gym and PyTorch for deep reinforcement learning
* Discover deep Q learning agents to solve discrete optimal control tasks
* Create custom learning environments for real-world problems
* Apply a deep actor-critic agent to drive a car autonomously in CARLA
* Use the latest learning environments and algorithms to upgrade your intelligent agent development skills

#### Who this book is for
If you’re a student, game/machine learning developer, or AI enthusiast looking to get started with building intelligent agents and algorithms to solve a variety of problems with the OpenAI Gym interface, this book is for you. You will also find this book useful if you want to learn how to build deep reinforcement learning-based agents to solve problems in your domain of interest. Though the book covers all the basic concepts that you need to know, some working knowledge of Python programming language will help you get the most out of it.
#### Table of Contents
1. Introduction to Intelligent Agents and Learning Environments
2. Reinforcement Learning and Deep Reinforcement Learning
3. Getting Started with OpenAI Gym and Deep Reinforcement Learning
4. Exploring the Gym and its Features
5. Implementing your First Learning Agent – Solving the Mountain Car problem
6. Implementing an Intelligent Agent for Optimal Control using Deep Q-Learning
7. Creating Custom OpenAI Gym Environments – Carla Driving Simulator
8. Implementing an Intelligent & Autonomous Car Driving Agent using Deep Actor-Critic Algorithm
9. Exploring the Learning Environment Landscape – Roboschool, Gym-Retro, StarCraft-II, DeepMindLab
10. Exploring the Learning Algorithm Landscape – DDPG (Actor-Critic), PPO (Policy-Gradient), Rainbow (Value-Based)

**
### Sinossi
**Implement intelligent agents using PyTorch to solve classic AI problems, play console games like Atari, and perform tasks such as autonomous driving using the CARLA driving simulator**
#### Key Features
* Explore the OpenAI Gym toolkit and interface to use over 700 learning tasks
* Implement agents to solve simple to complex AI problems
* Study learning environments and discover how to create your own

#### Book Description
Many real-world problems can be broken down into tasks that require a series of decisions to be made or actions to be taken. The ability to solve such tasks without a machine being programmed requires a machine to be artificially intelligent and capable of learning to adapt. This book is an easy-to-follow guide to implementing learning algorithms for machine software agents in order to solve discrete or continuous sequential decision making and control tasks. Hands-On Intelligent Agents with OpenAI Gym takes you through the process of building intelligent agent algorithms using deep reinforcement learning starting from the implementation of the building blocks for configuring, training, logging, visualizing, testing, and monitoring the agent. You will walk through the process of building intelligent agents from scratch to perform a variety of tasks. In the closing chapters, the book provides an overview of the latest learning environments and learning algorithms, along with pointers to more resources that will help you take your deep reinforcement learning skills to the next level.
#### What you will learn
* Explore intelligent agents and learning environments
* Understand the basics of RL and deep RL
* Get started with OpenAI Gym and PyTorch for deep reinforcement learning
* Discover deep Q learning agents to solve discrete optimal control tasks
* Create custom learning environments for real-world problems
* Apply a deep actor-critic agent to drive a car autonomously in CARLA
* Use the latest learning environments and algorithms to upgrade your intelligent agent development skills

#### Who this book is for
If you’re a student, game/machine learning developer, or AI enthusiast looking to get started with building intelligent agents and algorithms to solve a variety of problems with the OpenAI Gym interface, this book is for you. You will also find this book useful if you want to learn how to build deep reinforcement learning-based agents to solve problems in your domain of interest. Though the book covers all the basic concepts that you need to know, some working knowledge of Python programming language will help you get the most out of it.
#### Table of Contents
1. Introduction to Intelligent Agents and Learning Environments
2. Reinforcement Learning and Deep Reinforcement Learning
3. Getting Started with OpenAI Gym and Deep Reinforcement Learning
4. Exploring the Gym and its Features
5. Implementing your First Learning Agent – Solving the Mountain Car problem
6. Implementing an Intelligent Agent for Optimal Control using Deep Q-Learning
7. Creating Custom OpenAI Gym Environments – Carla Driving Simulator
8. Implementing an Intelligent & Autonomous Car Driving Agent using Deep Actor-Critic Algorithm
9. Exploring the Learning Environment Landscape – Roboschool, Gym-Retro, StarCraft-II, DeepMindLab
10. Exploring the Learning Algorithm Landscape – DDPG (Actor-Critic), PPO (Policy-Gradient), Rainbow (Value-Based)

### L’autore
**Praveen Palanisamy** works on developing autonomous intelligent systems. He is currently an AI researcher at General Motors R &D. He develops planning and decision-making algorithms and systems that use deep reinforcement learning for autonomous driving. Previously, he was at the Robotics Institute, Carnegie Mellon University, where he worked on autonomous navigation, including perception and AI for mobile robots. He has experience developing complete, autonomous, robotic systems from scratch.

**Implement intelligent agents using PyTorch to solve classic AI problems, play console games like Atari, and perform tasks such as autonomous driving using the CARLA driving simulator**
#### Key Features
* Explore the OpenAI Gym toolkit and interface to use over 700 learning tasks
* Implement agents to solve simple to complex AI problems
* Study learning environments and discover how to create your own

#### Book Description
Many real-world problems can be broken down into tasks that require a series of decisions to be made or actions to be taken. The ability to solve such tasks without a machine being programmed requires a machine to be artificially intelligent and capable of learning to adapt. This book is an easy-to-follow guide to implementing learning algorithms for machine software agents in order to solve discrete or continuous sequential decision making and control tasks. Hands-On Intelligent Agents with OpenAI Gym takes you through the process of building intelligent agent algorithms using deep reinforcement learning starting from the implementation of the building blocks for configuring, training, logging, visualizing, testing, and monitoring the agent. You will walk through the process of building intelligent agents from scratch to perform a variety of tasks. In the closing chapters, the book provides an overview of the latest learning environments and learning algorithms, along with pointers to more resources that will help you take your deep reinforcement learning skills to the next level.
#### What you will learn
* Explore intelligent agents and learning environments
* Understand the basics of RL and deep RL
* Get started with OpenAI Gym and PyTorch for deep reinforcement learning
* Discover deep Q learning agents to solve discrete optimal control tasks
* Create custom learning environments for real-world problems
* Apply a deep actor-critic agent to drive a car autonomously in CARLA
* Use the latest learning environments and algorithms to upgrade your intelligent agent development skills

#### Who this book is for
If you’re a student, game/machine learning developer, or AI enthusiast looking to get started with building intelligent agents and algorithms to solve a variety of problems with the OpenAI Gym interface, this book is for you. You will also find this book useful if you want to learn how to build deep reinforcement learning-based agents to solve problems in your domain of interest. Though the book covers all the basic concepts that you need to know, some working knowledge of Python programming language will help you get the most out of it.
#### Table of Contents
1. Introduction to Intelligent Agents and Learning Environments
2. Reinforcement Learning and Deep Reinforcement Learning
3. Getting Started with OpenAI Gym and Deep Reinforcement Learning
4. Exploring the Gym and its Features
5. Implementing your First Learning Agent – Solving the Mountain Car problem
6. Implementing an Intelligent Agent for Optimal Control using Deep Q-Learning
7. Creating Custom OpenAI Gym Environments – Carla Driving Simulator
8. Implementing an Intelligent & Autonomous Car Driving Agent using Deep Actor-Critic Algorithm
9. Exploring the Learning Environment Landscape – Roboschool, Gym-Retro, StarCraft-II, DeepMindLab
10. Exploring the Learning Algorithm Landscape – DDPG (Actor-Critic), PPO (Policy-Gradient), Rainbow (Value-Based)

**
### Sinossi
**Implement intelligent agents using PyTorch to solve classic AI problems, play console games like Atari, and perform tasks such as autonomous driving using the CARLA driving simulator**
#### Key Features
* Explore the OpenAI Gym toolkit and interface to use over 700 learning tasks
* Implement agents to solve simple to complex AI problems
* Study learning environments and discover how to create your own

#### Book Description
Many real-world problems can be broken down into tasks that require a series of decisions to be made or actions to be taken. The ability to solve such tasks without a machine being programmed requires a machine to be artificially intelligent and capable of learning to adapt. This book is an easy-to-follow guide to implementing learning algorithms for machine software agents in order to solve discrete or continuous sequential decision making and control tasks. Hands-On Intelligent Agents with OpenAI Gym takes you through the process of building intelligent agent algorithms using deep reinforcement learning starting from the implementation of the building blocks for configuring, training, logging, visualizing, testing, and monitoring the agent. You will walk through the process of building intelligent agents from scratch to perform a variety of tasks. In the closing chapters, the book provides an overview of the latest learning environments and learning algorithms, along with pointers to more resources that will help you take your deep reinforcement learning skills to the next level.
#### What you will learn
* Explore intelligent agents and learning environments
* Understand the basics of RL and deep RL
* Get started with OpenAI Gym and PyTorch for deep reinforcement learning
* Discover deep Q learning agents to solve discrete optimal control tasks
* Create custom learning environments for real-world problems
* Apply a deep actor-critic agent to drive a car autonomously in CARLA
* Use the latest learning environments and algorithms to upgrade your intelligent agent development skills

#### Who this book is for
If you’re a student, game/machine learning developer, or AI enthusiast looking to get started with building intelligent agents and algorithms to solve a variety of problems with the OpenAI Gym interface, this book is for you. You will also find this book useful if you want to learn how to build deep reinforcement learning-based agents to solve problems in your domain of interest. Though the book covers all the basic concepts that you need to know, some working knowledge of Python programming language will help you get the most out of it.
#### Table of Contents
1. Introduction to Intelligent Agents and Learning Environments
2. Reinforcement Learning and Deep Reinforcement Learning
3. Getting Started with OpenAI Gym and Deep Reinforcement Learning
4. Exploring the Gym and its Features
5. Implementing your First Learning Agent – Solving the Mountain Car problem
6. Implementing an Intelligent Agent for Optimal Control using Deep Q-Learning
7. Creating Custom OpenAI Gym Environments – Carla Driving Simulator
8. Implementing an Intelligent & Autonomous Car Driving Agent using Deep Actor-Critic Algorithm
9. Exploring the Learning Environment Landscape – Roboschool, Gym-Retro, StarCraft-II, DeepMindLab
10. Exploring the Learning Algorithm Landscape – DDPG (Actor-Critic), PPO (Policy-Gradient), Rainbow (Value-Based)

### L’autore
**Praveen Palanisamy** works on developing autonomous intelligent systems. He is currently an AI researcher at General Motors R &D. He develops planning and decision-making algorithms and systems that use deep reinforcement learning for autonomous driving. Previously, he was at the Robotics Institute, Carnegie Mellon University, where he worked on autonomous navigation, including perception and AI for mobile robots. He has experience developing complete, autonomous, robotic systems from scratch.

Only registered users can download this free product.

Hands-On Deep Learning for Games: Leverage the Power of Neural Networks and Reinforcement Learning to Build Intelligent Games

**Understand the core concepts of deep learning and deep reinforcement learning by applying them to develop games**
#### Key Features
* Apply the power of deep learning to complex reasoning tasks by building a Game AI
* Exploit the most recent developments in machine learning and AI for building smart games
* Implement deep learning models and neural networks with Python

#### Book Description
The number of applications of deep learning and neural networks has multiplied in the last couple of years. Neural nets has enabled significant breakthroughs in everything from computer vision, voice generation, voice recognition and self-driving cars. Game development is also a key area where these techniques are being applied. This book will give an in depth view of the potential of deep learning and neural networks in game development. We will take a look at the foundations of multi-layer perceptron’s to using convolutional and recurrent networks. In applications from GANs that create music or textures to self-driving cars and chatbots. Then we introduce deep reinforcement learning through the multi-armed bandit problem and other OpenAI Gym environments. As we progress through the book we will gain insights about DRL techniques such as Motivated Reinforcement Learning with Curiosity and Curriculum Learning. We also take a closer look at deep reinforcement learning and in particular the Unity ML-Agents toolkit. By the end of the book, we will look at how to apply DRL and the ML-Agents toolkit to enhance, test and automate your games or simulations. Finally, we will cover your possible next steps and possible areas for future learning.
#### What you will learn
* Learn the foundations of neural networks and deep learning.
* Use advanced neural network architectures in applications to create music, textures, self driving cars and chatbots.
* Understand the basics of reinforcement and DRL and how to apply it to solve a variety of problems.
* Working with Unity ML-Agents toolkit and how to install, setup and run the kit.
* Understand core concepts of DRL and the differences between discrete and continuous action environments.
* Use several advanced forms of learning in various scenarios from developing agents to testing games.

#### Who this book is for
This books is for game developers who wish to create highly interactive games by leveraging the power of machine and deep learning. No prior knowledge of machine learning, deep learning or neural networks is required this book will teach those concepts from scratch. A good understanding of Python is required.
#### Table of Contents
1. Deep Learning for Games
2. Convolutional and Recurrent Networks
3. GAN for Games
4. Building a Deep Learning Gaming Chatbot
5. Introducing DRL
6. Unity ML-Agents
7. Agent and the Environment
8. Understanding PPO
9. Rewards and Reinforcement Learning
10. Imitation and Transfer Learning
11. Building Multi-Agent Environments
12. Debugging/Testing a Game with DRL
13. Obstacle Tower Challenge and Beyond

**
### Sinossi
**Understand the core concepts of deep learning and deep reinforcement learning by applying them to develop games**
#### Key Features
* Apply the power of deep learning to complex reasoning tasks by building a Game AI
* Exploit the most recent developments in machine learning and AI for building smart games
* Implement deep learning models and neural networks with Python

#### Book Description
The number of applications of deep learning and neural networks has multiplied in the last couple of years. Neural nets has enabled significant breakthroughs in everything from computer vision, voice generation, voice recognition and self-driving cars. Game development is also a key area where these techniques are being applied. This book will give an in depth view of the potential of deep learning and neural networks in game development. We will take a look at the foundations of multi-layer perceptron’s to using convolutional and recurrent networks. In applications from GANs that create music or textures to self-driving cars and chatbots. Then we introduce deep reinforcement learning through the multi-armed bandit problem and other OpenAI Gym environments. As we progress through the book we will gain insights about DRL techniques such as Motivated Reinforcement Learning with Curiosity and Curriculum Learning. We also take a closer look at deep reinforcement learning and in particular the Unity ML-Agents toolkit. By the end of the book, we will look at how to apply DRL and the ML-Agents toolkit to enhance, test and automate your games or simulations. Finally, we will cover your possible next steps and possible areas for future learning.
#### What you will learn
* Learn the foundations of neural networks and deep learning.
* Use advanced neural network architectures in applications to create music, textures, self driving cars and chatbots.
* Understand the basics of reinforcement and DRL and how to apply it to solve a variety of problems.
* Working with Unity ML-Agents toolkit and how to install, setup and run the kit.
* Understand core concepts of DRL and the differences between discrete and continuous action environments.
* Use several advanced forms of learning in various scenarios from developing agents to testing games.

#### Who this book is for
This books is for game developers who wish to create highly interactive games by leveraging the power of machine and deep learning. No prior knowledge of machine learning, deep learning or neural networks is required this book will teach those concepts from scratch. A good understanding of Python is required.
#### Table of Contents
1. Deep Learning for Games
2. Convolutional and Recurrent Networks
3. GAN for Games
4. Building a Deep Learning Gaming Chatbot
5. Introducing DRL
6. Unity ML-Agents
7. Agent and the Environment
8. Understanding PPO
9. Rewards and Reinforcement Learning
10. Imitation and Transfer Learning
11. Building Multi-Agent Environments
12. Debugging/Testing a Game with DRL
13. Obstacle Tower Challenge and Beyond

### L’autore
**Micheal Lanham** is a proven software and tech innovator with 20 years of experience. During that time, he has developed a broad range of software applications in areas including games, graphics, web, desktop, engineering, artificial intelligence, GIS, and machine learning applications for a variety of industries as an R &D developer. At the turn of the millennium, Micheal began working with neural networks and evolutionary algorithms in game development. He was later introduced to Unity and has been an avid developer, consultant, manager, and author of multiple Unity games, graphic projects, and books ever since.

**Understand the core concepts of deep learning and deep reinforcement learning by applying them to develop games**
#### Key Features
* Apply the power of deep learning to complex reasoning tasks by building a Game AI
* Exploit the most recent developments in machine learning and AI for building smart games
* Implement deep learning models and neural networks with Python

#### Book Description
The number of applications of deep learning and neural networks has multiplied in the last couple of years. Neural nets has enabled significant breakthroughs in everything from computer vision, voice generation, voice recognition and self-driving cars. Game development is also a key area where these techniques are being applied. This book will give an in depth view of the potential of deep learning and neural networks in game development. We will take a look at the foundations of multi-layer perceptron’s to using convolutional and recurrent networks. In applications from GANs that create music or textures to self-driving cars and chatbots. Then we introduce deep reinforcement learning through the multi-armed bandit problem and other OpenAI Gym environments. As we progress through the book we will gain insights about DRL techniques such as Motivated Reinforcement Learning with Curiosity and Curriculum Learning. We also take a closer look at deep reinforcement learning and in particular the Unity ML-Agents toolkit. By the end of the book, we will look at how to apply DRL and the ML-Agents toolkit to enhance, test and automate your games or simulations. Finally, we will cover your possible next steps and possible areas for future learning.
#### What you will learn
* Learn the foundations of neural networks and deep learning.
* Use advanced neural network architectures in applications to create music, textures, self driving cars and chatbots.
* Understand the basics of reinforcement and DRL and how to apply it to solve a variety of problems.
* Working with Unity ML-Agents toolkit and how to install, setup and run the kit.
* Understand core concepts of DRL and the differences between discrete and continuous action environments.
* Use several advanced forms of learning in various scenarios from developing agents to testing games.

#### Who this book is for
This books is for game developers who wish to create highly interactive games by leveraging the power of machine and deep learning. No prior knowledge of machine learning, deep learning or neural networks is required this book will teach those concepts from scratch. A good understanding of Python is required.
#### Table of Contents
1. Deep Learning for Games
2. Convolutional and Recurrent Networks
3. GAN for Games
4. Building a Deep Learning Gaming Chatbot
5. Introducing DRL
6. Unity ML-Agents
7. Agent and the Environment
8. Understanding PPO
9. Rewards and Reinforcement Learning
10. Imitation and Transfer Learning
11. Building Multi-Agent Environments
12. Debugging/Testing a Game with DRL
13. Obstacle Tower Challenge and Beyond

**
### Sinossi
**Understand the core concepts of deep learning and deep reinforcement learning by applying them to develop games**
#### Key Features
* Apply the power of deep learning to complex reasoning tasks by building a Game AI
* Exploit the most recent developments in machine learning and AI for building smart games
* Implement deep learning models and neural networks with Python

#### Book Description
The number of applications of deep learning and neural networks has multiplied in the last couple of years. Neural nets has enabled significant breakthroughs in everything from computer vision, voice generation, voice recognition and self-driving cars. Game development is also a key area where these techniques are being applied. This book will give an in depth view of the potential of deep learning and neural networks in game development. We will take a look at the foundations of multi-layer perceptron’s to using convolutional and recurrent networks. In applications from GANs that create music or textures to self-driving cars and chatbots. Then we introduce deep reinforcement learning through the multi-armed bandit problem and other OpenAI Gym environments. As we progress through the book we will gain insights about DRL techniques such as Motivated Reinforcement Learning with Curiosity and Curriculum Learning. We also take a closer look at deep reinforcement learning and in particular the Unity ML-Agents toolkit. By the end of the book, we will look at how to apply DRL and the ML-Agents toolkit to enhance, test and automate your games or simulations. Finally, we will cover your possible next steps and possible areas for future learning.
#### What you will learn
* Learn the foundations of neural networks and deep learning.
* Use advanced neural network architectures in applications to create music, textures, self driving cars and chatbots.
* Understand the basics of reinforcement and DRL and how to apply it to solve a variety of problems.
* Working with Unity ML-Agents toolkit and how to install, setup and run the kit.
* Understand core concepts of DRL and the differences between discrete and continuous action environments.
* Use several advanced forms of learning in various scenarios from developing agents to testing games.

#### Who this book is for
This books is for game developers who wish to create highly interactive games by leveraging the power of machine and deep learning. No prior knowledge of machine learning, deep learning or neural networks is required this book will teach those concepts from scratch. A good understanding of Python is required.
#### Table of Contents
1. Deep Learning for Games
2. Convolutional and Recurrent Networks
3. GAN for Games
4. Building a Deep Learning Gaming Chatbot
5. Introducing DRL
6. Unity ML-Agents
7. Agent and the Environment
8. Understanding PPO
9. Rewards and Reinforcement Learning
10. Imitation and Transfer Learning
11. Building Multi-Agent Environments
12. Debugging/Testing a Game with DRL
13. Obstacle Tower Challenge and Beyond

### L’autore
**Micheal Lanham** is a proven software and tech innovator with 20 years of experience. During that time, he has developed a broad range of software applications in areas including games, graphics, web, desktop, engineering, artificial intelligence, GIS, and machine learning applications for a variety of industries as an R &D developer. At the turn of the millennium, Micheal began working with neural networks and evolutionary algorithms in game development. He was later introduced to Unity and has been an avid developer, consultant, manager, and author of multiple Unity games, graphic projects, and books ever since.

Only registered users can download this free product.

Hands-On Data Analysis with Pandas: Efficiently perform data collection, wrangling, analysis, and visualization using Python

**Get to grips with pandas—a versatile and high-performance Python library for data manipulation, analysis, and discovery**
#### Key Features
* Perform efficient data analysis and manipulation tasks using pandas
* Apply pandas to different real-world domains using step-by-step demonstrations
* Get accustomed to using pandas as an effective data exploration tool

#### Book Description
Data analysis has become a necessary skill in a variety of positions where knowing how to work with data and extract insights can generate significant value. Hands-On Data Analysis with Pandas will show you how to analyze your data, get started with machine learning, and work effectively with Python libraries often used for data science, such as pandas, NumPy, matplotlib, seaborn, and scikit-learn. Using real-world datasets, you will learn how to use the powerful pandas library to perform data wrangling to reshape, clean, and aggregate your data. Then, you will learn how to conduct exploratory data analysis by calculating summary statistics and visualizing the data to find patterns. In the concluding chapters, you will explore some applications of anomaly detection, regression, clustering, and classification, using scikit-learn, to make predictions based on past data. By the end of this book, you will be equipped with the skills you need to use pandas to ensure the veracity of your data, visualize it for effective decision-making, and reliably reproduce analyses across multiple datasets.
#### What you will learn
* Understand how data analysts and scientists gather and analyze data
* Perform data analysis and data wrangling in Python
* Combine, group, and aggregate data from multiple sources
* Create data visualizations with pandas, matplotlib, and seaborn
* Apply machine learning (ML) algorithms to identify patterns and make predictions
* Use Python data science libraries to analyze real-world datasets
* Use pandas to solve common data representation and analysis problems
* Build Python scripts, modules, and packages for reusable analysis code

#### Who this book is for
This book is for data analysts, data science beginners, and Python developers who want to explore each stage of data analysis and scientific computing using a wide range of datasets. You will also find this book useful if you are a data scientist who is looking to implement pandas in machine learning. Working knowledge of Python programming language will be beneficial.
#### Table of Contents
1. Introduction to Data Analysis
2. Working with Pandas DataFrames
3. Data Wrangling with Pandas
4. Aggregating Pandas DataFrames
5. Data Visualization with Pandas and Matplotlib
6. Plotting with Seaborn and Customization Techniques
7. Financial Analysis with Pandas: Bitcoin and the Stock Market
8. Rule-based Anomaly Detection: Catching Hackers
9. Getting started with Machine Learning in Python
10. Making Better Predictions: Optimizing ML Models
11. ML Anomaly Detection: Catching Hackers, Part 2
12. The Road Ahead

**
### Sinossi
**Get to grips with pandas—a versatile and high-performance Python library for data manipulation, analysis, and discovery**
#### Key Features
* Perform efficient data analysis and manipulation tasks using pandas
* Apply pandas to different real-world domains using step-by-step demonstrations
* Get accustomed to using pandas as an effective data exploration tool

#### Book Description
Data analysis has become a necessary skill in a variety of positions where knowing how to work with data and extract insights can generate significant value. Hands-On Data Analysis with Pandas will show you how to analyze your data, get started with machine learning, and work effectively with Python libraries often used for data science, such as pandas, NumPy, matplotlib, seaborn, and scikit-learn. Using real-world datasets, you will learn how to use the powerful pandas library to perform data wrangling to reshape, clean, and aggregate your data. Then, you will learn how to conduct exploratory data analysis by calculating summary statistics and visualizing the data to find patterns. In the concluding chapters, you will explore some applications of anomaly detection, regression, clustering, and classification, using scikit-learn, to make predictions based on past data. By the end of this book, you will be equipped with the skills you need to use pandas to ensure the veracity of your data, visualize it for effective decision-making, and reliably reproduce analyses across multiple datasets.
#### What you will learn
* Understand how data analysts and scientists gather and analyze data
* Perform data analysis and data wrangling in Python
* Combine, group, and aggregate data from multiple sources
* Create data visualizations with pandas, matplotlib, and seaborn
* Apply machine learning (ML) algorithms to identify patterns and make predictions
* Use Python data science libraries to analyze real-world datasets
* Use pandas to solve common data representation and analysis problems
* Build Python scripts, modules, and packages for reusable analysis code

#### Who this book is for
This book is for data analysts, data science beginners, and Python developers who want to explore each stage of data analysis and scientific computing using a wide range of datasets. You will also find this book useful if you are a data scientist who is looking to implement pandas in machine learning. Working knowledge of Python programming language will be beneficial.
#### Table of Contents
1. Introduction to Data Analysis
2. Working with Pandas DataFrames
3. Data Wrangling with Pandas
4. Aggregating Pandas DataFrames
5. Data Visualization with Pandas and Matplotlib
6. Plotting with Seaborn and Customization Techniques
7. Financial Analysis with Pandas: Bitcoin and the Stock Market
8. Rule-based Anomaly Detection: Catching Hackers
9. Getting started with Machine Learning in Python
10. Making Better Predictions: Optimizing ML Models
11. ML Anomaly Detection: Catching Hackers, Part 2
12. The Road Ahead

### L’autore
**Stefanie Molin** is a data scientist and software engineer at Bloomberg LP in NYC, tackling tough problems in information security, particularly revolving around anomaly detection, building tools for gathering data, and knowledge sharing. She has extensive experience in data science, designing anomaly detection solutions, and utilizing machine learning in both R and Python in the AdTech and FinTech industries. She holds a B.S. in operations research from Columbia University’s Fu Foundation School of Engineering and Applied Science, with minors in economics, and entrepreneurship and innovation. In her free time, she enjoys traveling the world, inventing new recipes, and learning new languages spoken among both people and computers.

**Get to grips with pandas—a versatile and high-performance Python library for data manipulation, analysis, and discovery**
#### Key Features
* Perform efficient data analysis and manipulation tasks using pandas
* Apply pandas to different real-world domains using step-by-step demonstrations
* Get accustomed to using pandas as an effective data exploration tool

#### Book Description
Data analysis has become a necessary skill in a variety of positions where knowing how to work with data and extract insights can generate significant value. Hands-On Data Analysis with Pandas will show you how to analyze your data, get started with machine learning, and work effectively with Python libraries often used for data science, such as pandas, NumPy, matplotlib, seaborn, and scikit-learn. Using real-world datasets, you will learn how to use the powerful pandas library to perform data wrangling to reshape, clean, and aggregate your data. Then, you will learn how to conduct exploratory data analysis by calculating summary statistics and visualizing the data to find patterns. In the concluding chapters, you will explore some applications of anomaly detection, regression, clustering, and classification, using scikit-learn, to make predictions based on past data. By the end of this book, you will be equipped with the skills you need to use pandas to ensure the veracity of your data, visualize it for effective decision-making, and reliably reproduce analyses across multiple datasets.
#### What you will learn
* Understand how data analysts and scientists gather and analyze data
* Perform data analysis and data wrangling in Python
* Combine, group, and aggregate data from multiple sources
* Create data visualizations with pandas, matplotlib, and seaborn
* Apply machine learning (ML) algorithms to identify patterns and make predictions
* Use Python data science libraries to analyze real-world datasets
* Use pandas to solve common data representation and analysis problems
* Build Python scripts, modules, and packages for reusable analysis code

#### Who this book is for
This book is for data analysts, data science beginners, and Python developers who want to explore each stage of data analysis and scientific computing using a wide range of datasets. You will also find this book useful if you are a data scientist who is looking to implement pandas in machine learning. Working knowledge of Python programming language will be beneficial.
#### Table of Contents
1. Introduction to Data Analysis
2. Working with Pandas DataFrames
3. Data Wrangling with Pandas
4. Aggregating Pandas DataFrames
5. Data Visualization with Pandas and Matplotlib
6. Plotting with Seaborn and Customization Techniques
7. Financial Analysis with Pandas: Bitcoin and the Stock Market
8. Rule-based Anomaly Detection: Catching Hackers
9. Getting started with Machine Learning in Python
10. Making Better Predictions: Optimizing ML Models
11. ML Anomaly Detection: Catching Hackers, Part 2
12. The Road Ahead

**
### Sinossi
**Get to grips with pandas—a versatile and high-performance Python library for data manipulation, analysis, and discovery**
#### Key Features
* Perform efficient data analysis and manipulation tasks using pandas
* Apply pandas to different real-world domains using step-by-step demonstrations
* Get accustomed to using pandas as an effective data exploration tool

#### Book Description
Data analysis has become a necessary skill in a variety of positions where knowing how to work with data and extract insights can generate significant value. Hands-On Data Analysis with Pandas will show you how to analyze your data, get started with machine learning, and work effectively with Python libraries often used for data science, such as pandas, NumPy, matplotlib, seaborn, and scikit-learn. Using real-world datasets, you will learn how to use the powerful pandas library to perform data wrangling to reshape, clean, and aggregate your data. Then, you will learn how to conduct exploratory data analysis by calculating summary statistics and visualizing the data to find patterns. In the concluding chapters, you will explore some applications of anomaly detection, regression, clustering, and classification, using scikit-learn, to make predictions based on past data. By the end of this book, you will be equipped with the skills you need to use pandas to ensure the veracity of your data, visualize it for effective decision-making, and reliably reproduce analyses across multiple datasets.
#### What you will learn
* Understand how data analysts and scientists gather and analyze data
* Perform data analysis and data wrangling in Python
* Combine, group, and aggregate data from multiple sources
* Create data visualizations with pandas, matplotlib, and seaborn
* Apply machine learning (ML) algorithms to identify patterns and make predictions
* Use Python data science libraries to analyze real-world datasets
* Use pandas to solve common data representation and analysis problems
* Build Python scripts, modules, and packages for reusable analysis code

#### Who this book is for
This book is for data analysts, data science beginners, and Python developers who want to explore each stage of data analysis and scientific computing using a wide range of datasets. You will also find this book useful if you are a data scientist who is looking to implement pandas in machine learning. Working knowledge of Python programming language will be beneficial.
#### Table of Contents
1. Introduction to Data Analysis
2. Working with Pandas DataFrames
3. Data Wrangling with Pandas
4. Aggregating Pandas DataFrames
5. Data Visualization with Pandas and Matplotlib
6. Plotting with Seaborn and Customization Techniques
7. Financial Analysis with Pandas: Bitcoin and the Stock Market
8. Rule-based Anomaly Detection: Catching Hackers
9. Getting started with Machine Learning in Python
10. Making Better Predictions: Optimizing ML Models
11. ML Anomaly Detection: Catching Hackers, Part 2
12. The Road Ahead

### L’autore
**Stefanie Molin** is a data scientist and software engineer at Bloomberg LP in NYC, tackling tough problems in information security, particularly revolving around anomaly detection, building tools for gathering data, and knowledge sharing. She has extensive experience in data science, designing anomaly detection solutions, and utilizing machine learning in both R and Python in the AdTech and FinTech industries. She holds a B.S. in operations research from Columbia University’s Fu Foundation School of Engineering and Applied Science, with minors in economics, and entrepreneurship and innovation. In her free time, she enjoys traveling the world, inventing new recipes, and learning new languages spoken among both people and computers.

Only registered users can download this free product.

Hands-On Blockchain for Python Developers

**Implement real-world decentralized applications using Python, Vyper, Populus, and Ethereum**
#### Key Features
* Stay up-to-date with everything you need to know about the blockchain ecosystem
* Implement smart contracts, wallets, and decentralized applications(DApps) using Python libraries
* Get deeper insights into storing content in a distributed storage platform

#### Book Description
Blockchain is seen as the main technological solution that works as a public ledger for all cryptocurrency transactions. This book serves as a practical guide to developing a full-fledged decentralized application with Python to interact with the various building blocks of blockchain applications. Hands-On Blockchain for Python Developers starts by demonstrating how blockchain technology and cryptocurrency hashing works. You will understand the fundamentals and benefits of smart contracts such as censorship resistance and transaction accuracy. As you steadily progress, you’ll go on to build smart contracts using Vyper, which has a similar syntax to Python. This experience will further help you unravel the other benefits of smart contracts, including reliable storage and backup, and efficiency. You’ll also use web3.py to interact with smart contracts and leverage the power of both the web3.py and Populus framework to build decentralized applications that offer security and seamless integration with cryptocurrencies. As you explore later chapters, you’ll learn how to create your own token on top of Ethereum and build a cryptocurrency wallet graphical user interface (GUI) that can handle Ethereum and Ethereum Request for Comments (ERC-20) tokens using the PySide2 library. This will enable users to seamlessly store, send, and receive digital money. Toward the end, you’ll implement InterPlanetary File System (IPFS) technology in your decentralized application to provide a peer-to-peer filesystem that can store and expose media. By the end of this book, you’ll be well-versed in blockchain programming and be able to build end-to-end decentralized applications on a range of domains using Python.
#### What you will learn
* Understand blockchain technology and what makes it an immutable database
* Use the features of web3.py API to interact with the smart contract
* Create your own cryptocurrency and token in Ethereum using Vyper
* Use IPFS features to store content on the decentralized storage platform
* Implement a Twitter-like decentralized application with a desktop frontend
* Build decentralized applications in the shape of console, web, and desktop applications

#### Who this book is for
If you are a Python developer who wants to enter the world of blockchain, Hands-On Blockchain for Python Developers is for you. The book will be your go-to guide to becoming well-versed with the blockchain ecosystem and building your own decentralized applications using Python and library support.
#### Table of Contents
1. Introduction to Blockchain Programming
2. Smart Contract Fundamentals
3. Implementing Smart Contract Using Vyper
4. Interacting With Smart Contract Using Web3
5. Populus Development Framework
6. Building a Practical Decentralized Application
7. Front-end Decentralized Application
8. Creating Token in Ethereum
9. Cryptocurrency Wallet
10. Inter Planetary: A Brave New File System
11. Using Py-ipfs-api to Connect to Decentralized File System
12. Implementing Decentralized Application Using IPFS

**

**Implement real-world decentralized applications using Python, Vyper, Populus, and Ethereum**
#### Key Features
* Stay up-to-date with everything you need to know about the blockchain ecosystem
* Implement smart contracts, wallets, and decentralized applications(DApps) using Python libraries
* Get deeper insights into storing content in a distributed storage platform

#### Book Description
Blockchain is seen as the main technological solution that works as a public ledger for all cryptocurrency transactions. This book serves as a practical guide to developing a full-fledged decentralized application with Python to interact with the various building blocks of blockchain applications. Hands-On Blockchain for Python Developers starts by demonstrating how blockchain technology and cryptocurrency hashing works. You will understand the fundamentals and benefits of smart contracts such as censorship resistance and transaction accuracy. As you steadily progress, you’ll go on to build smart contracts using Vyper, which has a similar syntax to Python. This experience will further help you unravel the other benefits of smart contracts, including reliable storage and backup, and efficiency. You’ll also use web3.py to interact with smart contracts and leverage the power of both the web3.py and Populus framework to build decentralized applications that offer security and seamless integration with cryptocurrencies. As you explore later chapters, you’ll learn how to create your own token on top of Ethereum and build a cryptocurrency wallet graphical user interface (GUI) that can handle Ethereum and Ethereum Request for Comments (ERC-20) tokens using the PySide2 library. This will enable users to seamlessly store, send, and receive digital money. Toward the end, you’ll implement InterPlanetary File System (IPFS) technology in your decentralized application to provide a peer-to-peer filesystem that can store and expose media. By the end of this book, you’ll be well-versed in blockchain programming and be able to build end-to-end decentralized applications on a range of domains using Python.
#### What you will learn
* Understand blockchain technology and what makes it an immutable database
* Use the features of web3.py API to interact with the smart contract
* Create your own cryptocurrency and token in Ethereum using Vyper
* Use IPFS features to store content on the decentralized storage platform
* Implement a Twitter-like decentralized application with a desktop frontend
* Build decentralized applications in the shape of console, web, and desktop applications

#### Who this book is for
If you are a Python developer who wants to enter the world of blockchain, Hands-On Blockchain for Python Developers is for you. The book will be your go-to guide to becoming well-versed with the blockchain ecosystem and building your own decentralized applications using Python and library support.
#### Table of Contents
1. Introduction to Blockchain Programming
2. Smart Contract Fundamentals
3. Implementing Smart Contract Using Vyper
4. Interacting With Smart Contract Using Web3
5. Populus Development Framework
6. Building a Practical Decentralized Application
7. Front-end Decentralized Application
8. Creating Token in Ethereum
9. Cryptocurrency Wallet
10. Inter Planetary: A Brave New File System
11. Using Py-ipfs-api to Connect to Decentralized File System
12. Implementing Decentralized Application Using IPFS

**

Only registered users can download this free product.

Hands-On Application Development with PyCharm: Accelerate your Python applications using practical coding techniques in PyCharm

**A definitive guide to PyCharm to help you build business-oriented Python applications ranging from modern web development to data science**
#### Key Features
* Learn basic to advanced PyCharm concepts to improve efficiency of your Python projects
* Work through practical examples that focus on efficient application development with PyCharm
* Explore advanced features in PyCharm such as code automation, version control, and GUI debugging

#### Book Description
JetBrain’s PyCharm is the most popular Integrated Development Environment (IDE) used by the Python community thanks to its numerous features that facilitate faster, more accurate, and more productive programming practices. However, the abundance of options and customizations can make PyCharm seem quite intimidating. Hands-on Application Development with PyCharm starts with PyCharm’s installation and configuration process, and systematically takes you through a number of its powerful features that can greatly improve your productivity. You’ll explore code automation, version control, graphical debugging/testing, management of virtual environments, and much more. Finally, you’ll delve into specific PyCharm features that support web development and data science, two of the fastest growing applications in Python programming. These include the integration of the Django framework as well as the extensive support for IPython and Jupyter Notebook. By the end of this PyCharm book, you will have gained extensive knowledge of the tool and be able to implement its features and make the most of its support for your projects.
#### What you will learn
* Explore PyCharm functionalities and what makes it stand out from other Python IDEs
* Set up, configure, and customize your Python projects in PyCharm
* Understand how PyCharm integrates with Django for web development
* Discover PyCharm’s capabilities in database management and data visualization
* Perform code automation, GUI testing, and version control in PyCharm
* Integrate interactive Python tools such as Jupyter Notebooks for building virtual environments

#### Who this book is for
If you’re a beginner or an expert Python user looking to improve your productivity using one of the best Python IDEs, this book is for you. Basic knowledge of Python programming language is expected.
#### Table of Contents
1. Introduction to PyCharm – The Most Popular IDE for Python
2. Installing and Configuring PyCharm
3. Customizing Interpreters and Virtual Environments
4. Editing and Formatting with Ease
5. Version Control with Git in PyCharm
6. Seamless Testing, Debugging, and Profiling
7. Web Development with JavaScript, HTML, and CSS
8. Integrating Django in PyCharm
9. Understanding Database Management with PyCharm
10. Building a Web Application in PyCharm
11. Turning On Scientific Mode
12. Dynamic Data Viewing with SciView and Jupyter
13. Building a Data Pipeline in PyCharm
14. More Possibilities with PyCharm Plugins
15. Future Developments

**

**A definitive guide to PyCharm to help you build business-oriented Python applications ranging from modern web development to data science**
#### Key Features
* Learn basic to advanced PyCharm concepts to improve efficiency of your Python projects
* Work through practical examples that focus on efficient application development with PyCharm
* Explore advanced features in PyCharm such as code automation, version control, and GUI debugging

#### Book Description
JetBrain’s PyCharm is the most popular Integrated Development Environment (IDE) used by the Python community thanks to its numerous features that facilitate faster, more accurate, and more productive programming practices. However, the abundance of options and customizations can make PyCharm seem quite intimidating. Hands-on Application Development with PyCharm starts with PyCharm’s installation and configuration process, and systematically takes you through a number of its powerful features that can greatly improve your productivity. You’ll explore code automation, version control, graphical debugging/testing, management of virtual environments, and much more. Finally, you’ll delve into specific PyCharm features that support web development and data science, two of the fastest growing applications in Python programming. These include the integration of the Django framework as well as the extensive support for IPython and Jupyter Notebook. By the end of this PyCharm book, you will have gained extensive knowledge of the tool and be able to implement its features and make the most of its support for your projects.
#### What you will learn
* Explore PyCharm functionalities and what makes it stand out from other Python IDEs
* Set up, configure, and customize your Python projects in PyCharm
* Understand how PyCharm integrates with Django for web development
* Discover PyCharm’s capabilities in database management and data visualization
* Perform code automation, GUI testing, and version control in PyCharm
* Integrate interactive Python tools such as Jupyter Notebooks for building virtual environments

#### Who this book is for
If you’re a beginner or an expert Python user looking to improve your productivity using one of the best Python IDEs, this book is for you. Basic knowledge of Python programming language is expected.
#### Table of Contents
1. Introduction to PyCharm – The Most Popular IDE for Python
2. Installing and Configuring PyCharm
3. Customizing Interpreters and Virtual Environments
4. Editing and Formatting with Ease
5. Version Control with Git in PyCharm
6. Seamless Testing, Debugging, and Profiling
7. Web Development with JavaScript, HTML, and CSS
8. Integrating Django in PyCharm
9. Understanding Database Management with PyCharm
10. Building a Web Application in PyCharm
11. Turning On Scientific Mode
12. Dynamic Data Viewing with SciView and Jupyter
13. Building a Data Pipeline in PyCharm
14. More Possibilities with PyCharm Plugins
15. Future Developments

**

Only registered users can download this free product.

Handmade Tile

***Handmade Tile* is a contemporary guide for ceramic artists and anyone interested in custom tile installations—from making, designing, and decorating to designing your space and installation.**
No matter how many years of experience you have as a ceramic artist or how many home-improvement projects you’ve tackled, nothing prepares you for the unique world of ceramic tile. From **concept** and **design** , through **firing** and **installation** , ceramic tiling is one of the few places in a home where art is permanently installed as a feature of a room.
In *Handmade Tile* , Forrest Lesch-Middelton shares everything he’s learned as the founder and owner of the custom tile business FLM Ceramics and Tile. From his years as a one-man operation to his current production facility, Forrest has seen it all and helps you every step of the way. Whether you want to **make your own tile** , or want to **use artistic and custom-made tile in your home** , this book has everything you need.
**Key features of the book include:**
* Making Tile: key tools, rolling, cutting, extruding
* Decorating: glazes, image transfer, cuerda seca, underglaze, slip
* Designing Your Space: tile in context, choosing your tile, codes and standards
* Installation: removing old tile, backing, preparing surfaces, setting, grouting

Galleries and interviews with **today’s top workings artists in tile** round out the package. Featured artists include Allison Bloom, Boris Aldridge, Disc Interiors, PV Tile, and more.

***Handmade Tile* is a contemporary guide for ceramic artists and anyone interested in custom tile installations—from making, designing, and decorating to designing your space and installation.**
No matter how many years of experience you have as a ceramic artist or how many home-improvement projects you’ve tackled, nothing prepares you for the unique world of ceramic tile. From **concept** and **design** , through **firing** and **installation** , ceramic tiling is one of the few places in a home where art is permanently installed as a feature of a room.
In *Handmade Tile* , Forrest Lesch-Middelton shares everything he’s learned as the founder and owner of the custom tile business FLM Ceramics and Tile. From his years as a one-man operation to his current production facility, Forrest has seen it all and helps you every step of the way. Whether you want to **make your own tile** , or want to **use artistic and custom-made tile in your home** , this book has everything you need.
**Key features of the book include:**
* Making Tile: key tools, rolling, cutting, extruding
* Decorating: glazes, image transfer, cuerda seca, underglaze, slip
* Designing Your Space: tile in context, choosing your tile, codes and standards
* Installation: removing old tile, backing, preparing surfaces, setting, grouting

Galleries and interviews with **today’s top workings artists in tile** round out the package. Featured artists include Allison Bloom, Boris Aldridge, Disc Interiors, PV Tile, and more.

Only registered users can download this free product.

Hair in All the Wrong Places

What has he done?
What’s happening to him?
And what on Earth is that smell?
For Colin Strauss, puberty stinks. Blackouts, hallucinations, and lapses in memory are the perils of growing up werewolf.
Worse than that, Colin worries he might have had something to do with the recent attacks on the townspeople. He may have eaten a person. It doesn’t matter that it’s someone he doesn’t particularly like. What kind of boy goes around eating people?
Foolishly, all Colin can think about is how Becca Emerson finally kissed him for the first time. Yep, hormones are afoot. Yikes!
But girls will have to wait. Collin better get himself under control before someone else ends up hurt or worse… dead.

What has he done?
What’s happening to him?
And what on Earth is that smell?
For Colin Strauss, puberty stinks. Blackouts, hallucinations, and lapses in memory are the perils of growing up werewolf.
Worse than that, Colin worries he might have had something to do with the recent attacks on the townspeople. He may have eaten a person. It doesn’t matter that it’s someone he doesn’t particularly like. What kind of boy goes around eating people?
Foolishly, all Colin can think about is how Becca Emerson finally kissed him for the first time. Yep, hormones are afoot. Yikes!
But girls will have to wait. Collin better get himself under control before someone else ends up hurt or worse… dead.

Only registered users can download this free product.

Hacking Made Easy: Hack Your Way Through Life One Step at a Time – Discover the Revolutionary Hacking Secrets of the 21st Century

*Have you ever wanted to rob a bank? Steal top secret details that could save the world? All this is illegal and dangerous but what if you really want to feel the thrill of breaking into safety valves and hidden treasures? This is possible only if you are a hacker. Hacking is the modern day equivalent of all the above but is something within the reach of everyone. Though perceived to be nerdy and only for geeks, hacking is a great pastime to utilize your creative energies and is the hottest trend right now. TV shows, movies and novels have added glamour and sophistication but is it something you can easily learn and possibly master? Yes! This eBook has made this mesmerizing field into an easy to understand guide for the novice and gradually amps up the expertise as the beginner progresses. With digestible tech jargon and all the latest in hacking software, this manual is a treat for professionals looking to refresh their knowledge along with its main purpose of introducing hacking to anyone who is interested in this 21st Century wonder of applied computing. The Chapters are sequential in their approach towards enabling the reader to improve their hacking abilities and also includes modern references to the latest trends in hacking at large.* **

*Have you ever wanted to rob a bank? Steal top secret details that could save the world? All this is illegal and dangerous but what if you really want to feel the thrill of breaking into safety valves and hidden treasures? This is possible only if you are a hacker. Hacking is the modern day equivalent of all the above but is something within the reach of everyone. Though perceived to be nerdy and only for geeks, hacking is a great pastime to utilize your creative energies and is the hottest trend right now. TV shows, movies and novels have added glamour and sophistication but is it something you can easily learn and possibly master? Yes! This eBook has made this mesmerizing field into an easy to understand guide for the novice and gradually amps up the expertise as the beginner progresses. With digestible tech jargon and all the latest in hacking software, this manual is a treat for professionals looking to refresh their knowledge along with its main purpose of introducing hacking to anyone who is interested in this 21st Century wonder of applied computing. The Chapters are sequential in their approach towards enabling the reader to improve their hacking abilities and also includes modern references to the latest trends in hacking at large.* **

Only registered users can download this free product.

Hacking Growth: How Today’s Fastest-Growing Companies Drive Breakout Success

**The definitive playbook by the pioneers of Growth Hacking, one of the hottest business methodologies in Silicon Valley and beyond.
**
It seems hard to believe today, but there was a time when Airbnb was the best-kept secret of travel hackers and couch surfers, Pinterest was a niche web site frequented only by bakers and crafters, LinkedIn was an exclusive network for C-suite executives and top-level recruiters, Facebook was MySpace &;s sorry step-brother, and Uber was a scrappy upstart that didn&;t stand a chance against the Goliath that was  New York City Yellow Cabs. 
So how did these companies grow from these humble beginnings into the powerhouses they are today? Contrary to popular belief, they didn&;t explode to massive worldwide popularity simply by building  a great product then crossing their fingers and hoping it would catch on. There was a studied, carefully implemented methodology behind these companies&; extraordinary rise. That methodology is called Growth Hacking, and it&;s practitioners include not just today&;s hottest start-ups, but also companies like IBM, Walmart, and Microsoft as well as the millions of entrepreneurs, marketers, managers and executives who make up the community of Growth Hackers.
Think of the Growth Hacking methodology as doing for market-share growth what Lean Start-Up did for product development, and Scrum did for productivity. It involves cross-functional teams and rapid-tempo testing and iteration that focuses *customers* : attaining them, retaining them, engaging them, and motivating them to come back and buy more.  
**
** An accessible and practical toolkit that teams and companies in all industries can use to increase their customer base and market share, this book walks readers through the process of creating and executing their own custom-made growth hacking strategy. It is a must read for any marketer, entrepreneur, innovator or manger looking to replace wasteful big bets and “spaghetti-on-the-wall” approaches with more consistent, replicable, cost-effective, and data-driven results. **

**The definitive playbook by the pioneers of Growth Hacking, one of the hottest business methodologies in Silicon Valley and beyond.
**
It seems hard to believe today, but there was a time when Airbnb was the best-kept secret of travel hackers and couch surfers, Pinterest was a niche web site frequented only by bakers and crafters, LinkedIn was an exclusive network for C-suite executives and top-level recruiters, Facebook was MySpace &;s sorry step-brother, and Uber was a scrappy upstart that didn&;t stand a chance against the Goliath that was  New York City Yellow Cabs. 
So how did these companies grow from these humble beginnings into the powerhouses they are today? Contrary to popular belief, they didn&;t explode to massive worldwide popularity simply by building  a great product then crossing their fingers and hoping it would catch on. There was a studied, carefully implemented methodology behind these companies&; extraordinary rise. That methodology is called Growth Hacking, and it&;s practitioners include not just today&;s hottest start-ups, but also companies like IBM, Walmart, and Microsoft as well as the millions of entrepreneurs, marketers, managers and executives who make up the community of Growth Hackers.
Think of the Growth Hacking methodology as doing for market-share growth what Lean Start-Up did for product development, and Scrum did for productivity. It involves cross-functional teams and rapid-tempo testing and iteration that focuses *customers* : attaining them, retaining them, engaging them, and motivating them to come back and buy more.  
**
** An accessible and practical toolkit that teams and companies in all industries can use to increase their customer base and market share, this book walks readers through the process of creating and executing their own custom-made growth hacking strategy. It is a must read for any marketer, entrepreneur, innovator or manger looking to replace wasteful big bets and “spaghetti-on-the-wall” approaches with more consistent, replicable, cost-effective, and data-driven results. **

Only registered users can download this free product.