Название: Introduction to Deep Learning Автор: Mauricio Alberto Ortega Ruiz Издательство: Toronto Academic Press Год: 2025 Страниц: 231 Язык: английский Формат: pdf (true) Размер: 41.9 MB
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.
Artificial Intelligence (AI) has been completely revolutionized by Deep Learning, which is now present in nearly all business applications. Machine Learning algorithms have access to a tremendous amount of data for research since nearly all information and transactions are now recorded digitally. But it’s difficult for conventional machine learning methods to investigate the complex correlations found in this so-called Big Data. This is especially true for unstructured data like text, speech, and image.
This book serves as an introductory guide to the field of Deep Learning, aiming to provide a comprehensive understanding of its principles, methodologies, and applications. Deep Learning, a subset of Machine Learning, has gained prominence due to its ability to automatically learn and extract intricate patterns from data, thereby enabling sophisticated tasks such as image and speech recognition, natural language processing (NLP), and autonomous decision-making.
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.
Chapter 5 introduces the PyTorch framework, focusing on tensors, gradients, and the construction of neural networks using its powerful APIs. The sixth chapter deals with generative Deep Learning techniques, including text generation, neural style transfer, and variational autoencoders. These methods enable the creation of new content such as images and text, showcasing the creative potential of Deep Learning models beyond traditional classification tasks. The seventh chapter is about advanced Deep Learning techniques, such as attention mechanisms and generative adversarial networks (GANs). These techniques enhance model performance by improving focus and generating realistic data, which is critical for tasks in natural language processing, computer vision, and creativity-driven applications. Chapter 8 concludes by examining the practical applications of Deep Learning in natural language processing and speech recognition. It covers topics like parsing, distributed representations, knowledge graphs, and multimodal learning, showcasing how Deep Learning transforms these domains by enabling accurate understanding and generation of human language.
This book has been written specifically for students and scholars to meet their needs in terms of knowledge and to provide them with a broad understanding of business information systems.
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Introduction to Deep Learning (2025) 
