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Deep Reinforcement Learning Hands-On

Deep Reinforcement Learning Hands-On by Maxim Lapan
**This practical guide will teach you how deep learning (DL) can be used to solve complex real-world problems. **
#### Key Features
* Explore deep reinforcement learning (RL), from the first principles to the latest algorithms
* Evaluate high-profile RL methods, including value iteration, deep Q-networks, policy gradients, TRPO, PPO, DDPG, D4PG, evolution strategies and genetic algorithms
* Keep up with the very latest industry developments, including AI-driven chatbots
#### Book Description
Recent developments in reinforcement learning (RL), combined with deep learning (DL), have seen unprecedented progress made towards training agents to solve complex problems in a human-like way. Google’s use of algorithms to play and defeat the well-known Atari arcade games has propelled the field to prominence, and researchers are generating new ideas at a rapid pace.
Deep Reinforcement Learning Hands-On is a comprehensive guide to the very latest DL tools and their limitations. You will evaluate methods including Cross-entropy and policy gradients, before applying them to real-world environments. Take on both the Atari set of virtual games and family favorites such as Connect4. The book provides an introduction to the basics of RL, giving you the know-how to code intelligent learning agents to take on a formidable array of practical tasks. Discover how to implement Q-learning on ‘grid world’ environments, teach your agent to buy and trade stocks, and find out how natural language models are driving the boom in chatbots.
#### What you will learn
* Understand the DL context of RL and implement complex DL models
* Learn the foundation of RL: Markov decision processes
* Evaluate RL methods including Cross-entropy, DQN, Actor-Critic, TRPO, PPO, DDPG, D4PG and others
* Discover how to deal with discrete and continuous action spaces in various environments
* Defeat Atari arcade games using the value iteration method
* Create your own OpenAI Gym environment to train a stock trading agent
* Teach your agent to play Connect4 using AlphaGo Zero
* Explore the very latest deep RL research on topics including AI-driven chatbots
#### Who this book is for
Some fluency in Python is assumed. Basic deep learning (DL) approaches should be familiar to readers and some practical experience in DL will be helpful. This book is an introduction to deep reinforcement learning (RL) and requires no background in RL.

Deep Learning with TensorFlow: Explore neural networks with Python

Delve into neural networks, implement deep learning algorithms, and explore layers of data abstraction with the help of this comprehensive TensorFlow guide About This Book * Learn how to implement advanced techniques in deep learning with Google’s brainchild, TensorFlow * Explore deep neural networks and layers of data abstraction with the help of this comprehensive guide * Real-world contextualization through some deep learning problems concerning research and application Who This Book Is For The book is intended for a general audience of people interested in machine learning and machine intelligence. A rudimentary level of programming in one language is assumed, as is a basic familiarity with computer science techniques and technologies, including a basic awareness of computer hardware and algorithms. Some competence in mathematics is needed to the level of elementary linear algebra and calculus. What You Will Learn * Learn about machine learning landscapes along with the historical development and progress of deep learning * Learn about deep machine intelligence and GPU computing with the latest TensorFlow 1.x * Access public datasets and utilize them using TensorFlow to load, process, and transform data * Use TensorFlow on real-world datasets, including images, text, and more * Learn how to evaluate the performance of your deep learning models * Using deep learning for scalable object detection and mobile computing * Train machines quickly to learn from data by exploring reinforcement learning techniques * Explore active areas of deep learning research and applications In Detail Deep learning is the step that comes after machine learning, and has more advanced implementations. Machine learning is not just for academics anymore, but is becoming a mainstream practice through wide adoption, and deep learning has taken the front seat. As a data scientist, if you want to explore data abstraction layers, this book will be your guide. This book shows how this can be exploited in the real world with complex raw data using TensorFlow 1.x. Throughout the book, you’ll learn how to implement deep learning algorithms for machine learning systems and integrate them into your product offerings, including search, image recognition, and language processing. Additionally, you’ll learn how to analyze and improve the performance of deep learning models. This can be done by comparing algorithms against benchmarks, along with machine intelligence, to learn from the information and determine ideal behaviors within a specific context. After finishing the book, you will be familiar with machine learning techniques, in particular the use of TensorFlow for deep learning, and will be ready to apply your knowledge to research or commercial projects. Style and approach This step-by-step guide will explore common, and not so common, deep neural networks and show how these can be exploited in the real world with complex raw data. With the help of practical examples, you will learn how to implement different types of neural nets to build smart applications related to text, speech, and image data processing.
**

Deep Learning With TensorFlow – Second Edition

Deep Learning with TensorFlow – Second Edition by Giancarlo Zaccone, Md. Rezaul Karim
**Delve into neural networks, implement deep learning algorithms, and explore layers of data abstraction with the help of TensorFlow.**
#### Key Features
* Learn how to implement advanced techniques in deep learning with Google’s brainchild, TensorFlow
* Explore deep neural networks and layers of data abstraction with the help of this comprehensive guide
* Gain real-world contextualization through some deep learning problems concerning research and application
#### Book Description
Deep learning is a branch of machine learning algorithms based on learning multiple levels of abstraction. Neural networks, which are at the core of deep learning, are being used in predictive analytics, computer vision, natural language processing, time series forecasting, and to perform a myriad of other complex tasks.
This book is conceived for developers, data analysts, machine learning practitioners and deep learning enthusiasts who want to build powerful, robust, and accurate predictive models with the power of TensorFlow, combined with other open source Python libraries.
Throughout the book, you’ll learn how to develop deep learning applications for machine learning systems using Feedforward Neural Networks, Convolutional Neural Networks, Recurrent Neural Networks, Autoencoders, and Factorization Machines. Discover how to attain deep learning programming on GPU in a distributed way.
You’ll come away with an in-depth knowledge of machine learning techniques and the skills to apply them to real-world projects.
#### What you will learn
* Apply deep machine intelligence and GPU computing with TensorFlow
* Access public datasets and use TensorFlow to load, process, and transform the data
* Discover how to use the high-level TensorFlow API to build more powerful applications
* Use deep learning for scalable object detection and mobile computing
* Train machines quickly to learn from data by exploring reinforcement learning techniques
* Explore active areas of deep learning research and applications
#### Who this book is for
The book is for people interested in machine learning and machine intelligence. A rudimentary level of programming in one language is assumed, as is a basic familiarity with computer science techniques and technologies, including a basic awareness of computer hardware and algorithms. Some competence in mathematics is needed to the level of elementary linear algebra and calculus.

Deep Learning with PyTorch

Build neural network models in text, vision and advanced analytics using PyTorch Key Features Learn PyTorch for implementing cutting-edge deep learning algorithms. Train your neural networks for higher speed and flexibility and learn how to implement them in various scenarios; Cover various advanced neural network architecture such as ResNet, Inception, DenseNet and more with practical examples; Book DescriptionDeep learning powers the most intelligent systems in the world, such as Google Voice, Siri, and Alexa. Advancements in powerful hardware, such as GPUs, software frameworks such as PyTorch, Keras, Tensorflow, and CNTK along with the availability of big data have made it easier to implement solutions to problems in the areas of text, vision, and advanced analytics. This book will get you up and running with one of the most cutting-edge deep learning libraries-PyTorch. PyTorch is grabbing the attention of deep learning researchers and data science professionals due to its accessibility, efficiency and being more native to Python way of development. You’ll start off by installing PyTorch, then quickly move on to learn various fundamental blocks that power modern deep learning. You will also learn how to use CNN, RNN, LSTM and other networks to solve real-world problems. This book explains the concepts of various state-of-the-art deep learning architectures, such as ResNet, DenseNet, Inception, and Seq2Seq, without diving deep into the math behind them. You will also learn about GPU computing during the course of the book. You will see how to train a model with PyTorch and dive into complex neural networks such as generative networks for producing text and images. By the end of the book, you’ll be able to implement deep learning applications in PyTorch with ease. What you will learn Use PyTorch for GPU-accelerated tensor computations Build custom datasets and data loaders for images and test the models using torchvision and torchtext Build an image classifier by implementing CNN architectures using PyTorch Build systems that do text classification and language modeling using RNN, LSTM, and GRU Learn advanced CNN architectures such as ResNet, Inception, Densenet, and learn how to use them for transfer learning Learn how to mix multiple models for a powerful ensemble model Generate new images using GAN’s and generate artistic images using style transfer Who this book is forThis book is for machine learning engineers, data analysts, data scientists interested in deep learning and are looking to explore implementing advanced algorithms in PyTorch. Some knowledge of machine learning is helpful but not a mandatory need. Working knowledge of Python programming is expected.
(source: Bol.com)

Deep Learning With Python

Discover the practical aspects of implementing deep-learning solutions using the rich Python ecosystem. This book bridges the gap between the academic state-of-the-art and the industry state-of-the-practice by introducing you to deep learning frameworks such as Keras, Theano, and Caffe. The practicalities of these frameworks is often acquired by practitioners by reading source code, manuals, and posting questions on community forums, which tends to be a slow and a painful process. *Deep Learning with Python *allows you to ramp up to such practical know-how in a short period of time and focus more on the domain, models, and algorithms.
This book briefly covers the mathematical prerequisites and fundamentals of deep learning, making this book a good starting point for software developers who want to get started in deep learning. A brief survey of deep learning architectures is also included.
*Deep Learning with Python* also introduces you to key concepts of automatic differentiation and GPU computation which, while not central to deep learning, are critical when it comes to conducting large scale experiments. 
**What You Will Learn ** Leverage deep learning frameworks in Python namely, Keras, Theano, and Caffe 
Gain the fundamentals of deep learning with mathematical prerequisites 
Discover the practical considerations of large scale experiments 
Take deep learning models to production
**Who This Book Is For**
Software developers who want to try out deep learning as a practical solution to a particular problem. Software developers in a data science team who want to take deep learning models developed by data scientists to production.

Deep Learning with Applications Using Python

Deep Learning with Applications Using Python: Chatbots and Face, Object, and Speech Recognition With TensorFlow and Keras by Navin Kumar Manaswi
Explore deep learning applications, such as computer vision, speech recognition, and chatbots, using frameworks such as TensorFlow and Keras. This book helps you to ramp up your practical know-how in a short period of time and focuses you on the domain, models, and algorithms required for deep learning applications. *Deep Learning with Applications Using Python* covers topics such as chatbots, natural language processing, and face and object recognition. The goal is to equip you with the concepts, techniques, and algorithm implementations needed to create programs capable of performing deep learning.
This book covers convolutional neural networks, recurrent neural networks, and multilayer perceptrons. It also discusses popular APIs such as IBM Watson, Microsoft Azure, and scikit-learn.
**What You Will Learn **
* Work with various deep learning frameworks such as TensorFlow, Keras, and scikit-learn.
* Use face recognition and face detection capabilities
* Create speech-to-text and text-to-speech functionality
* Engage with chatbots using deep learning
**Who This Book Is For**
Data scientists and developers who want to adapt and build deep learning applications.

Deep Learning Quick Reference

Deep Learning Quick Reference by Mike Bernico
**Dive deeper into neural networks and get your models trained, optimized with this quick reference guide**
#### Key Features
* A quick reference to all important deep learning concepts and their implementations
* Essential tips, tricks, and hacks to train a variety of deep learning models such as CNNs, RNNs, LSTMs, and more
* Supplemented with essential mathematics and theory, every chapter provides best practices and safe choices for training and fine-tuning your models in Keras and Tensorflow.
#### Book Description
Deep learning has become an essential necessity to enter the world of artificial intelligence. With this book deep learning techniques will become more accessible, practical, and relevant to practicing data scientists. It moves deep learning from academia to the real world through practical examples.
You will learn how Tensor Board is used to monitor the training of deep neural networks and solve binary classification problems using deep learning. Readers will then learn to optimize hyperparameters in their deep learning models. The book then takes the readers through the practical implementation of training CNN’s, RNN’s, and LSTM’s with word embeddings and seq2seq models from scratch. Later the book explores advanced topics such as Deep Q Network to solve an autonomous agent problem and how to use two adversarial networks to generate artificial images that appear real. For implementation purposes, we look at popular Python-based deep learning frameworks such as Keras and Tensorflow, Each chapter provides best practices and safe choices to help readers make the right decision while training deep neural networks.
By the end of this book, you will be able to solve real-world problems quickly with deep neural networks.
#### What you will learn
* Solve regression and classification challenges with TensorFlow and Keras
* Learn to use Tensor Board for monitoring neural networks and its training
* Optimize hyperparameters and safe choices/best practices
* Build CNN’s, RNN’s, and LSTM’s and using word embedding from scratch
* Build and train seq2seq models for machine translation and chat applications.
* Understanding Deep Q networks and how to use one to solve an autonomous agent problem.
* Explore Deep Q Network and address autonomous agent challenges.
#### Who this book is for
If you are a Data Scientist or a Machine Learning expert, then this book is a very useful read in training your advanced machine learning and deep learning models. You can also refer this book if you are stuck in-between the neural network modeling and need immediate assistance in getting accomplishing the task smoothly. Some prior knowledge of Python and tight hold on the basics of machine learning is required.

Deep Learning for Computer Vision

Deep Learning for Computer Vision by Rajalingappaa shanmugamani
**Learn how to model and train advanced neural networks to implement a variety of Computer Vision tasks**
#### Key Features
* Train different kinds of deep learning model from scratch to solve specific problems in Computer Vision
* Combine the power of Python, Keras, and TensorFlow to build deep learning models for object detection, image classification, similarity learning, image captioning, and more
* Includes tips on optimizing and improving the performance of your models under various constraints
#### Book Description
Deep learning has shown its power in several application areas of Artificial Intelligence, especially in Computer Vision. Computer Vision is the science of understanding and manipulating images, and finds enormous applications in the areas of robotics, automation, and so on. This book will also show you, with practical examples, how to develop Computer Vision applications by leveraging the power of deep learning.
In this book, you will learn different techniques related to object classification, object detection, image segmentation, captioning, image generation, face analysis, and more. You will also explore their applications using popular Python libraries such as TensorFlow and Keras. This book will help you master state-of-the-art, deep learning algorithms and their implementation.
#### What you will learn
* Set up an environment for deep learning with Python, TensorFlow, and Keras
* Define and train a model for image and video classification
* Use features from a pre-trained Convolutional Neural Network model for image retrieval
* Understand and implement object detection using the real-world Pedestrian Detection scenario
* Learn about various problems in image captioning and how to overcome them by training images and text together
* Implement similarity matching and train a model for face recognition
* Understand the concept of generative models and use them for image generation
* Deploy your deep learning models and optimize them for high performance
#### Who this book is for
This book is targeted at data scientists and Computer Vision practitioners who wish to apply the concepts of Deep Learning to overcome any problem related to Computer Vision. A basic knowledge of programming in Python—and some understanding of machine learning concepts—is required to get the best out of this book.

Deep Learning Essentials

Get to grips with the essentials of deep learning by leveraging the power of Python Key Features Your one-stop solution to get started with the essentials of deep learning and neural network modeling Train different kinds of neural networks to tackle various problems in Natural Language Processing, computer vision, speech recognition, and more Covers popular Python libraries such as Tensorflow, Keras, and more, along with tips on training, deploying and optimizing your deep learning models in the best possible manner Book DescriptionDeep Learning a trending topic in the field of Artificial Intelligence today and can be considered to be an advanced form of machine learning, which is quite tricky to master. This book will help you take your first steps in training efficient deep learning models and applying them in various practical scenarios. You will model, train, and deploy different kinds of neural networks such as Convolutional Neural Network, Recurrent Neural Network, and will see some of their applications in real-world domains including computer vision, natural language processing, speech recognition, and so on. You will build practical projects such as chatbots, implement reinforcement learning to build smart games, and develop expert systems for image captioning and processing. Popular Python library such as TensorFlow is used in this book to build the models. This book also covers solutions for different problems you might come across while training models, such as noisy datasets, small datasets, and more. This book does not assume any prior knowledge of deep learning. By the end of this book, you will have a firm understanding of the basics of deep learning and neural network modeling, along with their practical applications. What you will learn Get to grips with the core concepts of deep learning and neural networks Set up deep learning library such as TensorFlow Fine-tune your deep learning models for NLP and Computer Vision applications Unify different information sources, such as images, text, and speech through deep learning Optimize and fine-tune your deep learning models for better performance Train a deep reinforcement learning model that plays a game better than humans Learn how to make your models get the best out of your GPU or CPU Who this book is forAspiring data scientists and machine learning experts who have limited or no exposure to deep learning will find this book to be very useful. If you are looking for a resource that gets you up and running with the fundamentals of deep learning and neural networks, this book is for you. As the models in the book are trained using the popular Python-based libraries such as Tensorflow and Keras, it would be useful to have sound programming knowledge of Python.
(source: Bol.com)

Deep Learning Cookbook

Deep Learning Cookbook: Practical Recipes to Get Started Quickly by Douwe Osinga
Deep learning doesn’t have to be intimidating. Until recently, this machine-learning method required years of study, but with frameworks such as Keras and Tensorflow, software engineers without a background in machine learning can quickly enter the field. With the recipes in this cookbook, you’ll learn how to solve deep-learning problems for classifying and generating text, images, and music.
Each chapter consists of several recipes needed to complete a single project, such as training a music recommending system. Author Douwe Osinga also provides a chapter with half a dozen techniques to help you if you’re stuck. Examples are written in Python with code available on GitHub as a set of Python notebooks.
You’ll learn how to:
* Create applications that will serve real users
* Use word embeddings to calculate text similarity
* Build a movie recommender system based on Wikipedia links
* Learn how AIs see the world by visualizing their internal state
* Build a model to suggest emojis for pieces of text
* Reuse pretrained networks to build an inverse image search service
* Compare how GANs, autoencoders and LSTMs generate icons
* Detect music styles and index song collections

Deep Green Resistance

For years, Derrick Jensen has asked his audiences, “Do you think this culture will undergo a voluntary transformation to a sane and sustainable way of life?” No one ever says yes.
*Deep Green Resistance* starts where the environmental movement leaves off: industrial civilization is incompatible with life. Technology can’t fix it, and shopping – no matter how green – won’t stop it. To save this planet, we need a serious resistance movement that can bring down the industrial economy. *Deep Green Resistance* evaluates strategic options for resistance, from nonviolence to guerrilla warfare, and the conditions required for those options to be successful. It provides an exploration of organizational structures, recruitment, security, and target selection for both aboveground and underground action. *Deep Green Resistance* also discusses a culture of resistance and the crucial support role that it can play.
*Deep Green Resistance* is a plan of action for anyone determined to fight for this planet – and win.

The Deeds of the Disturber

SUMMARY: Can fear kill? There are those who believe so–but Amelia Peabody is skeptical. A respected Egyptologist and amateur sleuth, Amelia has foiled felonious schemes from Victoria’s England to the Middle East. And she doubts that it was a Nineteenth-Dynasty mummy’s curse that caused the death of a night watchman in the British Museum. The corpse was found sprawled in the mummy’s shadow, a look of terror frozen on the guard’s face. What–or who–killed the unfortunate man is a mystery that seems too intriguingly delicious for Amelia to pass up, especially now that she, her dashing archaeologist husband, Emerson, and their precocious son, Ramses, are back on Britain’s shores. But a contemporary curse can be as lethal as one centuries old–and the foggy London thoroughfares can be as treacherous as the narrow, twisting alleyways of Cairo after dark–when a perpetrator of evil deeds sets his murderous sights on his relentless pursuer…Amelia Peabody! Can fear kill? There are those who believe so–but Amelia Peabody is skeptical. A respected Egyptologist and amateur sleuth, Amelia has foiled felonious schemes from Victoria’s England to the Middle East. And she doubts that it was a Nineteenth-Dynasty mummy’s curse that caused the death of a night watchman in the British Museum. The corpse was found sprawled in the mummy’s shadow, a look of terror frozen on the guard’s face. What–or who–killed the unfortunate man is a mystery that seems too intriguingly delicious for Amelia to pass up, especially now that she, her dashing archaeologist husband, Emerson, and their precocious son, Ramses, are back on Britain’s shores. But a contemporary curse can be as lethal as one centuries old–and the foggy London thoroughfares can be as treacherous as the narrow, twisting alleyways of Cairo after dark–when a perpetrator of evil deeds sets his murderous sights on his relentless pursuer…Amelia Peabody!

Dedicated Alpha

[Siren Everlasting Classic ManLove: Erotic Alternative Paranormal Romance, M/M, shape-shifters, light bondage, sex toys, HEA] He was willing to suffer a broken heart to protect the man he loved… Getting a second chance at love is more than Luke could ask for, but discovering Dillinger is a cop has him running for the door. Luke has a secret that could destroy Dillinger’s career, but keeping his distance from the sexy officer might be more difficult than Luke ever imagined. Dillinger was dedicated to uncovering the truth behind Twin Pines College, but when he reconnects to Luke, a man he never forgot, he is willing to let his goal slide. Luke is more than Dillinger could have ever hoped for, but just as suddenly as he reappeared, Luke vanishes. Each man is willing to suffer to protect the other, but when their resistance to their own fate puts an innocent man in jeopardy, the leader of the Twin Pines Grizzlies has to intervene. The only question is, will he be too late? ** A Siren Erotic Romance

Deceptions

Koren Bladewell now knows he is a wizard, but he is lost in the wilderness, cut off from help and unable to use his powers. The enemy is poised at the border with an overwhelming invasion force, and it will take wizards, soldiers and a young, untested princess to hold off defeat long enough for Koren to be trained and grow into his power. But he might not wish to help those who deceived him and destroyed his life

Deception Point

From the *New York Times* bestselling author of *The Da Vinci Code* comes an explosive, high-tech thriller that takes readers from the chilling depths of the Arctic Ocean to the treacherous heights of Washington power.
When a new NASA satellite spots evidence of an astonishingly rare object buried deep in the Arctic ice, the floundering space agency proclaims a much-needed victory…a victory that has profound implications for U.S. space policy and the impending presidential election. With the Oval Office in the balance, the President dispatches White House Intelligence analyst Rachel Sexton to the Milne Ice Shelf to verify the authenticity of the find. Accompanied by a team of experts, including the charismatic academic Michael Tolland, Rachel uncovers the unthinkable: evidence of scientific trickery — a bold deception that threatens to plunge the world into controversy.
But before Rachel can contact the President, she and Michael are attacked by a deadly team of assassins controlled by a mysterious power broker who will stop at nothing to hide the truth. Fleeing for their lives in an environment as desolate as it is lethal, their only hope for survival is to find out who is behind this masterful ploy. The truth, they will learn, is the most shocking deception of all.
In *Deception Point,* bestselling author Dan Brown transports readers from the ultrasecret National Reconnaissance Office to the towering ice shelves of the Arctic Circle, and back again to the hallways of power inside the West Wing. Heralded for masterfully intermingling science, history, and politics in his critically acclaimed, blockbuster thrillers*Angels & Demons* and *The Da Vinci Code,* Brown has crafted a novel in which nothing is as it seems — and behind every corner is a stunning surprise.*Deception Point* is pulse-pounding fiction at its best.
**Recensie(s)**

A case study in suspense. Unputdownable * Washington Post * Intrigue and menace mingle in one of the finest mysteries I’ve ever read. An amazing tale with enigma piled on secrets stacked on riddles — Clive Cussler
(source: Bol.com)

Debt of Honor

SUMMARY: Called out of retirement to serve as National Security Advisor to the president, Jack Ryan, with the help of CIA officers John Clark and Domingo Chavez, must prepare the untested president to meet the challenges of a new world order. Reissue.