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Hands-On Reinforcement Learning with Python

A hands-on guide enriched with examples to master deep reinforcement learning algorithms with Python Key Features Your entry point into the;

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PyTorch Deep Learning Hands-On

Hands-on projects cover all the key deep learning methods built step-by-step in PyTorch Key Features Internals and principles of PyTorch;

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Reinforcement Learning for Finance

This book introduces reinforcement learning with mathematical theory and practical examples from quantitative finance using the TensorFlow;

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Deep Reinforcement Learning Hands-On

Reinforcement Learning Hands-On, Second Edition is an updated and expanded version of the bestselling guide to the very latest reinforcement learning;

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Grokking Deep Reinforcement Learning

learn based on the responses of the environment. * Foundational reinforcement learning concepts and methods * The most;

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Python Reinforcement Learning Projects

even build other neural networks. By the end of this book, you will have hands-on experience with eight reinforcement learning projects, each;

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Deep Reinforcement Learning with Python

Deep reinforcement learning is a fast-growing discipline that is making a significant impact in fields of autonomous vehicles, robotics;

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Deep Reinforcement Learning Hands-On

Reinforcement Learning Hands-On is a comprehensive guide to the very latest DL tools and their limitations. You will evaluate methods including Cross;

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TensorFlow 2 Reinforcement Learning Cookbook

Discover recipes for developing AI applications to solve a variety of real-world business problems using reinforcement learning Key;

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Zai:Deep Reinforcement Learning i_p

cannot. Deep Reinforcement Learning in Action teaches you the fundamental concepts and terminology of deep reinforcement learning, along;

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Practical MATLAB Deep Learning

deep learning and reinforcement learning. Over the course of the book, you'll learn to model complex systems and apply deep learning to;

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Practical MATLAB Deep Learning

deep learning and reinforcement learning. Over the course of the book, you'll learn to model complex systems and apply deep learning to;

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Reinforcement Learning

Master reinforcement learning, a popular area of machine learning, starting with the basics: discover how agents and the environment evolve;

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Python Reinforcement Learning

Learning Path includes content from the following Packt products: Hands-On Reinforcement Learning with Python by Sudharsan Ravichandiran Python;

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Hands-On Deep Learning for Games

Understand the core concepts of deep learning and deep reinforcement learning by applying them to develop games Key Features Apply the;

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Foundations of Deep Reinforcement Learning

The Contemporary Introduction to Deep Reinforcement Learning that Combines Theory and Practice Deep reinforcement learning (deep RL;

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Deep Learning Illustrated

want to start leveraging its power, but previous books on deep learning have often been non-intuitive, inaccessible, and dry. In Deep Learning;

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Hands-On Reinforcement Learning for Games

Explore reinforcement learning (RL) techniques to build cutting-edge games using Python libraries such as PyTorch, OpenAI Gym, and;

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Hands-On Intelligent Agents with OpenAI Gym

algorithms using deep reinforcement learning starting from the implementation of the building blocks for configuring, training, logging, visualizing;

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Deep Reinforcement Learning

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The The Reinforcement Learning Workshop

Start with the basics of reinforcement learning and explore deep learning concepts such as deep Q-learning, deep recurrent Q-networks, and;

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Deep Learning and Practice with MindSpore

This book systematically introduces readers to the theory of deep learning and explores its practical applications based on the MindSpore;

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Deep Learning and Practice with MindSpore

This book systematically introduces readers to the theory of deep learning and explores its practical applications based on the MindSpore;

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Deep Reinforcement Learning

gone unnoticed by educators. Many universities have begun offering courses on the subject of deep reinforcement learning. The aim of this book;

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Advanced Deep Learning with Keras

, GANs, VAEs, and deep reinforcement learning that drive today's most impressive AI results. Key Features Explore the most advanced deep;

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Introduction to Machine Learning

convolutional and generative adversarial networks; new material in the chapter on reinforcement learning that covers the use of deep networks, the;

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Hands-On Deep Learning with R

with using reinforcement learning and deep reinforcement learning to solve various real-world problems. By the end of this deep learning book;

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