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Implement reinforcement learning techniques and algorithms with the help of real-world examples and recipes Key Features Use PyTorch 1.x to;
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PyTorch 1.x to build advanced neural network models Learn to perform a wide range of tasks by implementing deep learning algorithms and;
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Implement key deep learning methods in PyTorch: CNNs, GANs, RNNs, reinforcement learning, and more Build deep learning workflows and take deep;
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deep reinforcement learning implementation using popular deep learning frameworks such as TensorFlow and PyTorch. In the end, you'll;
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intelligence applications. Starting with an introduction to the fundamentals of deep reinforcement learning and TensorFlow 2.x, the book covers;
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networks using PyTorch. This new edition covers also topics such as Scorch, a compatible module equivalent to the Scikit machine learning library;
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Get up to speed with the deep learning concepts of Pytorch using a problem-solution approach. Starting with an introduction to PyTorch;
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insights from text by building deep learning models for natural language processing (NLP) tasks. Starting by understanding how to install PyTorch;
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the critical concepts of applying transfer learning to images, debugging models, and PyTorch in production. Learn how to deploy deep;
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, and PyTorch Key Features Learn and implement machine learning algorithms in a variety of real-life scenarios Cover a range of tasks catering;
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started quickly and then grows with you as you, and your deep learning skills, become more sophisticated. Deep Learning with PyTorch;
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Introduction to deep learning and PyTorch by building a convolutional neural network and recurrent neural network for real-world use cases;
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Get a head start in the world of AI and deep learning by developing your skills with PyTorch Key Features Learn how to define your own;
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deep learning libraries-PyTorch. PyTorch is grabbing the attention of deep learning researchers and data science professionals due to its;
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Master the practical aspects of implementing deep learning solutions with PyTorch, using a hands-on approach to understanding both theory;
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Master the practical aspects of implementing deep learning solutions with PyTorch, using a hands-on approach to understanding both theory;
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spend searching for answers. Research scientists, machine learning engineers, and software developers will find clear, structured PyTorch;
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Explore reinforcement learning (RL) techniques to build cutting-edge games using Python libraries such as PyTorch, OpenAI Gym, and;
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OpenNI 1.x programmers who want to move to new versions of OpenNI can use this book as a starting point. This book uses C++ as the primary;
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grips with the core principles, you'll explore real-world examples and implementations of one-shot learning using PyTorch 1.x on datasets such;
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learning environments Understand the basics of RL and deep RL Get started with OpenAI Gym and PyTorch for deep reinforcement learning Discover deep;
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and implement tricky neural networks using Google's TensorFlow 1.x * An easy-to-follow guide that lets you explore reinforcement learning;
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will be able to build your own computer vision projects using transfer learning and PyTorch. What You Will Learn Solve problems;
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Reinforcement learning (RL) will deliver one of the biggest breakthroughs in AI over the next decade, enabling algorithms to learn from;
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Master reinforcement learning, a popular area of machine learning, starting with the basics: discover how agents and the environment evolve;
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