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sector of the economy. The deep learning revolution has brought us driverless cars, the greatly improved Google Translate, fluent conversations;
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, methods of developing leaders now require equal revolution. Using a multidimensional case-study research design, Deep Learning explores the;
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The application of deep learning methods to problems in natural language processing has generated significant progress across a wide range;
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The application of deep learning methods to problems in natural language processing has generated significant progress across a wide range;
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At the dawn of the 4th Industrial Revolution, the field of Deep Learning (a sub-field of Artificial Intelligence and Machine Learning;
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Baidu, we are often interacting with a deep learning system. In this volume in the MIT Press Essential Knowledge series, computer scientist John;
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developing domains of computer technology. Deep learning algorithms have brought a revolution in computer vision applications by introducing an;
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Dive into the future of data science and learn how to build the sophisticated algorithms that are fundamental to deep learning and AI with;
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Deep learning algorithms have brought a revolution to the computer vision community by introducing non-traditional and efficient solutions;
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and transaction. Coupled with immense stores of data, maturity in CPU and GPU hardware, the invention of new, open source deep learning;
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is driving the AI revolution, and PyTorch is making it easier than ever before for anyone to build deep learning applications. This PyTorch book will;
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Deep learning algorithms have brought a revolution to the computer vision community by introducing non-traditional and efficient solutions;
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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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analysts who have performed machine learning tasks and now want to explore deep learning and want a quick reference that could address the pain;
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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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Deploy deep learning solutions in production with ease using TensorFlow. You'll also develop the mathematical understanding and intuition;
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learning algorithms, along with Monte Carlo tree search (MCTS), which played a key role in the success of AlphaGo. The final chapters conclude with;
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and Challenges provides readers with a focused approach for the design and implementation of deep learning concepts using data analytics;
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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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concentration on advances of deep learning architectures. The book discusses various artificial intelligence (AI) techniques based on deep learning;
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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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. The purpose of this book is to show readers how to create a learning environment that promotes deep learning in their classes. The book will;
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. The purpose of this book is to show readers how to create a learning environment that promotes deep learning in their classes. The book will;
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with all the necessary basics to get started in deep learning. Chapter 1 introduces you to the world of deep learning and its difference from;
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Einde inhoud
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