Introduction to Deep Learning by Mauricio Alberto Ortega Ruiz (.PDF)

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File Size: 41.9 MB

Introduction to Deep Learning by Mauricio Alberto Ortega Ruiz
Requirements: .PDF reader, 41.9 MB
Overview: This book is designed to provide a comprehensive introduction to the field of Deep Learning, covering its foundational principles, techniques, and applications. It covers topics such as neural networks, convolutional networks, recurrent networks, and deep reinforcement learning. The content emphasizes both the theoretical concepts and practical implementations of Deep Learning models, providing insights into how these models are trained and applied to solve complex problems. Practical examples and hands-on exercises are included to help readers develop a solid understanding of Deep learning techniques and their applications in various fields. This book comprises eight chapters; the first chapter discusses the fundamental concepts and historical evolution of Deep Learning, as well as the framework and application of Deep Learning. The second chapter expands on deep neural network models, exploring multilayer perceptrons, convolutional neural networks, recurrent neural networks, and other advanced architectures like Boltzmann machines and deep autoencoders. Chapter 3 shifts focus to deep reinforcement learning techniques, elucidating algorithms such as Q-learning, deep Q-networks, and policy gradient methods. The fourth chapter specializes in convolutional neural networks (CNNs), offering a detailed examination of their components such as filters, pooling, and padding. It also discusses the practical implementation of CNNs using TensorFlow.
Genre: Non-Fiction > Tech & Devices

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