Python by Sudharsan Ravichandiran (104 results)
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- Softcover
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- Softcover
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- Softcover
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Paperback or Softback. Condition: New. Hands-On Reinforcement Learning with Python: Master reinforcement and deep reinforcement learning using OpenAI Gym and TensorFlow. Book.
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- Softcover
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- Softcover
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Python Reinforcement Learning: Solve complex real-world problems by mastering reinforcement learning algorithms using OpenAI Gym and TensorFlow
Sudharsan Ravichandiran,Yang Wenzhuo,Rajalingappaa Shanmugamani,Sean Saito
- Softcover
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- Softcover
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Paperback or Softback. Condition: New. Hands-On Meta Learning with Python. Book.
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- Softcover
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- Softcover
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Paperback. Condition: new. Paperback. Understand basic to advanced deep learning algorithms, the mathematical principles behind them, and their practical applications.Key FeaturesGet up-to-speed with building your own neural networks from scratch Gain insights into the mathematical principles behind deep learning algorithmsImple…ment popular deep learning algorithms such as CNNs, RNNs, and more using TensorFlowBook DescriptionDeep learning is one of the most popular domains in the AI space, allowing you to develop multi-layered models of varying complexities. This book introduces you to popular deep learning algorithmsfrom basic to advancedand shows you how to implement them from scratch using TensorFlow. Throughout the book, you will gain insights into each algorithm, the mathematical principles behind it, and how to implement it in the best possible manner. The book starts by explaining how you can build your own neural networks, followed by introducing you to TensorFlow, the powerful Python-based library for machine learning and deep learning. Moving on, you will get up to speed with gradient descent variants, such as NAG, AMSGrad, AdaDelta, Adam, and Nadam. The book will then provide you with insights into RNNs and LSTM and how to generate song lyrics with RNN. Next, you will master the math for convolutional and capsule networks, widely used for image recognition tasks. Then you learn how machines understand the semantics of words and documents using CBOW, skip-gram, and PV-DM. Afterward, you will explore various GANs, including InfoGAN and LSGAN, and autoencoders, such as contractive autoencoders and VAE. By the end of this book, you will be equipped with all the skills you need to implement deep learning in your own projects.What you will learnImplement basic-to-advanced deep learning algorithmsMaster the mathematics behind deep learning algorithmsBecome familiar with gradient descent and its variants, such as AMSGrad, AdaDelta, Adam, and NadamImplement recurrent networks, such as RNN, LSTM, GRU, and seq2seq modelsUnderstand how machines interpret images using CNN and capsule networksImplement different types of generative adversarial network, such as CGAN, CycleGAN, and StackGANExplore various types of autoencoder, such as Sparse autoencoders, DAE, CAE, and VAEWho this book is forIf you are a machine learning engineer, data scientist, AI developer, or simply want to focus on neural networks and deep learning, this book is for you. Those who are completely new to deep learning, but have some experience in machine learning and Python programming, will also find the book very helpful. This book introduces basic-to-advanced deep learning algorithms used in a production environment by AI researchers and principal data scientists; it explains algorithms intuitively, including the underlying math, and shows how to implement them using popular Python-based deep learning libraries such as TensorFlow. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
- Softcover
Seller: GreatBookPrices, Columbia, U.S.A.GreatBookPrices
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- Softcover
Seller: Rarewaves.com USA, London, United KingdomRarewaves.com USA
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Digital. Condition: New. A hands-on guide enriched with examples to master deep reinforcement learning algorithms with PythonAbout This Book. 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 archit…ectures along with mathWho This Book Is ForIf 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.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 DQNIn DetailReinforcement 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.Style and approachThis is a hands-on book designed to further expand your machine learning skills by understanding reinforcement to deep reinforcement learning algorithms with applications in Python.
- Softcover
Seller: GreatBookPrices, Columbia, U.S.A.GreatBookPrices
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- Softcover
Seller: BargainBookStores, Grand Rapids, U.S.A.BargainBookStores
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Paperback or Softback. Condition: New. Deep Reinforcement Learning with Python - Second Edition. Book.
- Softcover
Seller: California Books, Miami, U.S.A.California Books
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- Softcover
Seller: GreatBookPrices, Columbia, U.S.A.GreatBookPrices
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- Softcover
Seller: Rarewaves.com USA, London, United KingdomRarewaves.com USA
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Paperback. Condition: New. Understand basic-to-advanced deep learning algorithms, the mathematical principles behind them, and their practical applicationsKey FeaturesGet up to speed with building your own neural networks from scratchGain insights into the mathematical principles behind deep learning algorithmsImplement popular…deep learning algorithms such as CNNs, RNNs, and more using TensorFlowBook DescriptionDeep learning is one of the most popular domains in the AI space that allows you to develop multi-layered models of varying complexities. This book introduces you to popular deep learning algorithms-from basic to advanced-and shows you how to implement them from scratch using TensorFlow. Throughout the book, you will gain insights into each algorithm, the mathematical principles involved, and how to implement it in the best possible manner. The book starts by explaining how you can build your own neural networks, followed by introducing you to TensorFlow, the powerful Python-based library for machine learning and deep learning. Moving on, you will get up to speed with gradient descent variants, such as NAG, AMSGrad, AdaDelta, Adam, and Nadam. The book will then provide you with insights into recurrent neural networks (RNNs) and LSTM and how to generate song lyrics with RNN. Next, you will master the math necessary to work with convolutional and capsule networks, widely used for image recognition tasks. You will also learn how machines understand the semantics of words and documents using CBOW, skip-gram, and PV-DM. Finally, you will explore GANs, including InfoGAN and LSGAN, and autoencoders, such as contractive autoencoders and VAE. By the end of this book, you will be equipped with all the skills you need to implement deep learning in your own projects.What you will learnImplement basic-to-advanced deep learning algorithmsMaster the mathematics behind deep learning algorithmsBecome familiar with gradient descent and its variants, such as AMSGrad, AdaDelta, Adam, and NadamImplement recurrent networks, such as RNN, LSTM, GRU, and seq2seq modelsUnderstand how machines interpret images using CNN and capsule networksImplement different types of generative adversarial network, such as CGAN, CycleGAN, and StackGANExplore various types of autoencoder, such as Sparse autoencoders, DAE, CAE, and VAEWho this book is forIf you are a machine learning engineer, data scientist, AI developer, or anyone looking to delve into neural networks and deep learning, this book is for you. Those who are completely new to deep learning, but have some experience in machine learning and Python programming will also find the book very helpful.
- Softcover
Seller: Brit Books, Milton Keynes, United KingdomBrit Books
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Python Reinforcement Learning
Sudharsan Ravichandiran; Sean Saito; Rajalingappaa Shanmugamani; Yang Wenzhuo
- Softcover
Seller: GreatBookPrices, Columbia, U.S.A.GreatBookPrices
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- Softcover
Seller: BargainBookStores, Grand Rapids, U.S.A.BargainBookStores
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Paperback or Softback. Condition: New. Python Reinforcement Learning. Book.
- Softcover
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- Softcover
Seller: Ria Christie Collections, Uxbridge, United KingdomRia Christie Collections
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Condition: New. In.
