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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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This book introduces reinforcement learning with mathematical theory and practical examples from quantitative finance using the TensorFlow;
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Learning with Python introduces you to the theory behind reinforcement learning (RL) algorithms and the code that will be used to implement them;
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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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looking at Open AI Gym. You'll then learn about Swarm Intelligence with Python in terms of reinforcement learning. The last part of the book;
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Learning Path includes content from the following Packt products: Hands-On Reinforcement Learning with Python by Sudharsan Ravichandiran Python;
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Beginning-Intermediate;...
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reinforcement learning. You will also build deep learning workflows with the PyTorch framework, migrate models built in Python to highly efficient;
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various reinforcement learning techniques and algorithms play an important role in game development with Python. Starting with the basics, this;
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models using transfer learning. Advanced Analytics with Python, 2nd edition features a chapter on clustering with a neural network;
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learning models using transfer learning. Advanced Analytics with Python, 2nd edition features a chapter on clustering with a neural network;
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reinforcement learning Who this book is forBuilding Machine Learning Systems with Python is for data scientists, machine learning developers, and;
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A guide to advances in machine learning for financial professionals, with working Python code Key Features Explore advances in machine;
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edition of the bestselling introduction to deep reinforcement learning, expanded with six new chapters Learn advanced exploration techniques;
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models using Python libraries such as DEAP, scikit-learn, and NumPy Key Features Explore the ins and outs of genetic algorithms with this fast;
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machine learning applications using modern libraries from the Python ecosystem. The book will also guide you on how to implement various machine;
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Get up and running with artificial intelligence in no time using hands-on problem-solving recipes Explore popular Python libraries and tools;
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can be trained with machine learning methods through a simple-to-use Python API. This book takes you from the basics of Reinforcement and Q;
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of intelligence and games, the book is organized into chapters on reinforcement learning, heuristic planning, adaptive sampling, function approximation;
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with Python Scikit-Learn library is explained to audience with an emphasis on supervised learning and classification. Image processing on IoT;
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with Python Scikit-Learn library is explained to audience with an emphasis on supervised learning and classification. Image processing on IoT;
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This book covers the fundamentals of machine learning with Python in a concise and dynamic manner. It covers data mining and large-scale;
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, healthcare, finance, and many more. This book covers deep reinforcement learning using deep-q learning and policy gradient models with coding;
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Applied machine learning with a solid foundation in theory. Revised and expanded for TensorFlow 2, GANs, and reinforcement learning;
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Python skills and a basic understanding of deep learning. About the technology Deep reinforcement learning is a form of machine;
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for building self-learning agents Work with advanced Reinforcement Learning concepts and algorithms such as imitation learning and evolution;
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), and recommender systems Review reinforcement learning and CNN Who This Book Is For Python developers, data engineers, and machine;
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