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Deep learning is one of today's hottest fields. This approach to machine learning is achieving breakthrough results in some of today's;
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This book covers algorithmic and hardware implementation techniques to enable embedded deep learning. The authors describe synergetic;
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models. Deep Learning techniques are introduced through theory, comprehensively illustrated, explained through the TensorFlow source code;
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In this richly illustrated book, deep neural network learning algorithms are explained informally first, followed by detailed mathematical;
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This book systematically introduces readers to the theory of deep learning and explores its practical applications based on the MindSpore;
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This book systematically introduces readers to the theory of deep learning and explores its practical applications based on the MindSpore;
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Harness the power of MATLAB for deep-learning challenges. Practical MATLAB Deep Learning, Second Edition, remains a one-of a-kind book that;
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Harness the power of MATLAB for deep-learning challenges. Practical MATLAB Deep Learning, Second Edition, remains a one-of a-kind book that;
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Powerful, independent recipes to build deep learning models in different application areas using R libraries About This Book * Master;
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Deep learning, a branch of Artificial Intelligence and machine learning, has led to new approaches to solving problems in a variety;
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Deep reinforcement learning is a fast-growing discipline that is making a significant impact in fields of autonomous vehicles, robotics;
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This book provides readers with a comprehensive and recent exposition in deep learning and its multidisciplinary applications, with a;
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Deploy deep learning solutions in production with ease using TensorFlow. You'll also develop the mathematical understanding and intuition;
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Speed up the design and implementation of deep learning solutions using Apache Spark Key Features Explore the world of distributed deep;
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This book provides a complete illustration of deep learning concepts with case-studies and practical examples useful for real time;
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identifying cats in YouTube videos. Powerful deep learning techniques can efficiently extract insight from the kind of structured data collected by;
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on an end-to-end approach to developing supervised learning algorithms in regression and classification with practical business-centric use;
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Implement neural network models in R 3.5 using TensorFlow, Keras, and MXNet Key Features Use R 3.5 for building deep learning models for;
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Discover best practices for choosing, building, training, and improving deep learning models using Keras-R, and TensorFlow-R libraries Key;
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Deep learning is a committed approach to learning. It is a process of constructing and interpreting new knowledge in light of prior;
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Deep learning is a committed approach to learning. It is a process of constructing and interpreting new knowledge in light of prior;
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Take a deep dive into deep learning Deep learning provides the means for discerning patterns in the data that drive online business;
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Explore TensorFlow's capabilities to perform efficient deep learning on images Key Features Discover image processing for machine vision;
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explores the cutting edge of deep learning studies. A subfield of machine learning, deep learning differs from conventional machine learning;
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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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Learn how to harness modern deep-learning methods in many contexts. Packed with intuitive theory, practical implementation methods, and;
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