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comprehensible) introduction to the two fundamental algorithms in deep learning: gradient descent and backpropagation. Finally, Kelleher considers the;
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provides an introduction to deep learning and using MATLAB's deep-learning toolboxes. In this book, you'll see how these toolboxes provide the;
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provides an introduction to deep learning and using MATLAB's deep-learning toolboxes. In this book, yoĆ¼ll see how these toolboxes provide the;
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This book begins with an introduction to AI, followed by machine learning, deep learning, NLP, and reinforcement learning. Readers will;
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FEATURES * Practical code examples * In-depth introduction to Keras * Teaches the difference between Deep Learning and AI ABOUT THE;
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The Contemporary Introduction to Deep Reinforcement Learning that Combines Theory and Practice Deep reinforcement learning (deep RL;
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genetics and genomics Apply deep learning to understand biophysical systems Get a brief introduction to machine learning with DeepChem Use;
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personal assistants, automated translation, and autonomous vehicles. An Introduction to Deep Learning Business Applications for Developers covers;
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, performance evaluation, Deep Learning optimization, deep auto-encoders, and deep neural networksProvides readers with an introduction to Deep;
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Introduction to Machine Learning with Applications in Information Security, Second Edition provides a classroom-tested introduction to a;
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In this monograph, the authors present an introduction to the framework of variational autoencoders (VAEs) that provides a principled;
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of online learners. You'll start with an introduction to AI, where you'll learn the history of neural networks and what sets deep learning apart;
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optimization. End-of-chapter exercises help readers to apply concepts learned. Introduction to Machine Learning can be used in courses for advanced;
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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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Learn how to train popular deep learning architectures such as autoencoders, convolutional and recurrent neural networks while discovering;
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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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edition of the bestselling introduction to deep reinforcement learning, expanded with six new chapters Learn advanced exploration techniques;
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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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that are otherwise invisible to us. Deep Learning from the Basics begins with a fast-paced introduction to deep learning with Python, its;
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expand your skillset. Starting with an introduction to basic machine learning algorithms, to give you a solid foundation, Deep Learning with;
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statistical and traditional machine learning methods for anomaly detection using Scikit-Learn in Python, the book then provides an introduction to;
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to different groups of readers and their expertise. The three parts are: Machine Learning, NLP, and Speech Introduction The first part has;
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to different groups of readers and their expertise. The three parts are: Machine Learning, NLP, and Speech Introduction The first part has;
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This book provides a survey of deep learning approaches to domain adaptation in computer vision. It gives the reader an overview of the;
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An introduction to a broad range of topics in deep learning, covering mathematical and conceptual background, deep learning techniques used;
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This textbook offers a comprehensive introduction to Machine Learning techniques and algorithms. This Third Edition covers newer approaches;
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