Deep Learning Foundations

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Deep Learning

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Author : Christopher M. Bishop
language : en
Publisher: Springer Nature
Release Date : 2023-11-01

Deep Learning written by Christopher M. Bishop and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-11-01 with Computers categories.

This book offers a comprehensive introduction to the central ideas that underpin deep learning. It is intended both for newcomers to machine learning and for those already experienced in the field. Covering key concepts relating to contemporary architectures and techniques, this essential book equips readers with a robust foundation for potential future specialization. The field of deep learning is undergoing rapid evolution, and therefore this book focusses on ideas that are likely to endure the test of time. The book is organized into numerous bite-sized chapters, each exploring a distinct topic, and the narrative follows a linear progression, with each chapter building upon content from its predecessors. This structure is well-suited to teaching a two-semester undergraduate or postgraduate machine learning course, while remaining equally relevant to those engaged in active research or in self-study. A full understanding of machine learning requires some mathematical background and so the book includes a self-contained introduction to probability theory. However, the focus of the book is on conveying a clear understanding of ideas, with emphasis on the real-world practical value of techniques rather than on abstract theory. Complex concepts are therefore presented from multiple complementary perspectives including textual descriptions, diagrams, mathematical formulae, and pseudo-code. Chris Bishop is a Technical Fellow at Microsoft and is the Director of Microsoft Research AI4Science. He is a Fellow of Darwin College Cambridge, a Fellow of the Royal Academy of Engineering, and a Fellow of the Royal Society. Hugh Bishop is an Applied Scientist at Wayve, a deep learning autonomous driving company in London, where he designs and trains deep neural networks. He completed his MPhil in Machine Learning and Machine Intelligence at Cambridge University. “Chris Bishop wrote a terrific textbook on neural networks in 1995 and has a deep knowledge of the field and its core ideas. His many years of experience in explaining neural networks have made him extremely skillful at presenting complicated ideas in the simplest possible way and it is a delight to see these skills applied to the revolutionary new developments in the field.” -- Geoffrey Hinton "With the recent explosion of deep learning and AI as a research topic, and the quickly growing importance of AI applications, a modern textbook on the topic was badly needed. The "New Bishop" masterfully fills the gap, covering algorithms for supervised and unsupervised learning, modern deep learning architecture families, as well as how to apply all of this to various application areas." – Yann LeCun “This excellent and very educational book will bring the reader up to date with the main concepts and advances in deep learning with a solid anchoring in probability. These concepts are powering current industrial AI systems and are likely to form the basis of further advances towards artificial general intelligence.” -- Yoshua Bengio

Deep Learning Foundations

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Author : Taeho Jo
language : en
Publisher: Springer Nature
Release Date : 2023-07-25

Deep Learning Foundations written by Taeho Jo and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-07-25 with Technology & Engineering categories.

This book provides a conceptual understanding of deep learning algorithms. The book consists of the four parts: foundations, deep machine learning, deep neural networks, and textual deep learning. The first part provides traditional supervised learning, traditional unsupervised learning, and ensemble learning, as the preparation for studying deep learning algorithms. The second part deals with modification of existing machine learning algorithms into deep learning algorithms. The book’s third part deals with deep neural networks, such as Multiple Perceptron, Recurrent Networks, Restricted Boltzmann Machine, and Convolutionary Neural Networks. The last part provides deep learning techniques that are specialized for text mining tasks. The book is relevant for researchers, academics, students, and professionals in machine learning.

Deep Learning Foundations Of Neural Networks

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Author : Muni Srinivas Degala
language : en
Publisher: OrangeBooks Publication
Release Date : 2024-12-07

Deep Learning Foundations Of Neural Networks written by Muni Srinivas Degala and has been published by OrangeBooks Publication this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-12-07 with Computers categories.

This book is designed to simplify and demystify the complex concepts of Machine Learning, and Deep Learning. It provides an easy-to-understand approach for students and beginners, breaking down difficult topics into digestible pieces. With clear explanations, real-life examples, and practical insights, this book serves as an essential guide for anyone eager to explore the transformative technologies shaping the future. It is perfect for learners seeking to grasp deep learning concepts efficiently.

Deep Learning Foundations And Advancements

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Author : Dr. Gali Nageswara Rao
language : en
Publisher: RK Publication
Release Date : 2024-10-01

Deep Learning Foundations And Advancements written by Dr. Gali Nageswara Rao and has been published by RK Publication this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-10-01 with Computers categories.

Deep Learning: Foundations and Advancements a comprehensive exploration of the core principles and cutting-edge developments in deep learning. This foundational topics such as neural networks, optimization techniques, and learning algorithms, while also delving into advanced applications and research, including reinforcement learning, generative models, and deep neural architectures. With a focus on both theory and practical implementation, it offers readers a solid understanding of how deep learning is transforming industries like computer vision, natural language processing, and autonomous systems.

Mastering Machine Learning And Deep Learning

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Author : Krishna Kant Singh
language : en
Publisher: Wiley
Release Date : 2024-06-18

Mastering Machine Learning And Deep Learning written by Krishna Kant Singh and has been published by Wiley this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-06-18 with Computers categories.

Deep Learning Foundations Powering Ai With Neural Networks

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Author : Isandro Myles
language : en
Publisher: Independently Published
Release Date : 2025-09-09

Deep Learning Foundations Powering Ai With Neural Networks written by Isandro Myles and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-09-09 with Computers categories.

Unlock the power of neural networks and deep learning for AI-driven solutions. In Deep Learning Foundations, you'll learn how to build and deploy deep learning models that power applications in image recognition, natural language processing (NLP), and more. Whether you're a beginner or looking to deepen your knowledge, this book will guide you through the fundamentals of deep learning and help you apply them to real-world AI challenges. Inside, you'll discover how to: Understand the basics of deep learning: learn about neural networks, activation functions, loss functions, and backpropagation. Implement feedforward networks and convolutional neural networks (CNNs) for tasks like image classification and object detection. Dive into recurrent neural networks (RNNs) and LSTMs for sequence data like time series and text. Master transfer learning and leverage pre-trained models for faster, more efficient model development. Develop NLP models using Word2Vec, BERT, and GPT for sentiment analysis, text generation, and language translation. Use TensorFlow and Keras for building, training, and deploying deep learning models. Apply regularization techniques like dropout, batch normalization, and early stopping to avoid overfitting. Evaluate models using metrics like accuracy, precision, recall, F1 score, and more. Fine-tune models with hyperparameter optimization, learning rate schedules, and grid/random search. Learn how to work with real-world datasets and deploy models for production-ready AI solutions. Packed with hands-on projects, step-by-step examples, and real-world applications, this book will provide you with the tools to build advanced AI models for industries like healthcare, finance, entertainment, and more. Who This Book Is For Beginners interested in learning deep learning and AI Data scientists and machine learning engineers looking to apply deep learning to practical problems Researchers and students exploring AI-driven solutions in image recognition and NLP Developers seeking to implement deep learning models in real-world applications Master the foundations of deep learning and build AI models that solve complex problems with cutting-edge techniques.

Handbook Of Research On Machine Learning

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Author : Monika Mangla
language : en
Publisher: CRC Press
Release Date : 2022-08-04

Handbook Of Research On Machine Learning written by Monika Mangla and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-08-04 with Computers categories.

This volume takes the reader on a technological voyage of machine learning advancements, highlighting the systematic changes in algorithms, challenges, and constraints. The technological advancements in the ML arena have transformed and revolutionized several fields, including transportation, agriculture, finance, weather monitoring, and others. This book brings together researchers, authors, industrialists, and academicians to cover a vast selection of topics in ML, starting with the rudiments of machine learning approaches and going on to specific applications in healthcare and industrial automation. The book begins with an overview of the ethics, security and privacy issues, future directions, and challenges in machine learning as well as a systematic review of deep learning techniques and provides an understanding of building generative adversarial networks. Chapters explore predictive data analytics for health issues. The book also adds a macro dimension by highlighting the industrial applications of machine learning, such as in the steel industry, for urban information retrieval, in garbage detection, in measuring air pollution, for stock market predictions, for underwater fish detection, as a fake news predictor, and more.

Deep Learning Foundations Natural Language Processing With Tensorflow

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Author : Harshit Tyagi
language : en
Publisher:
Release Date : 2021

Deep Learning Foundations Natural Language Processing With Tensorflow written by Harshit Tyagi and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021 with categories.

There is a growing demand to harness the power of natural language processing (NLP) and deep learning models to be able to make sense of textual data and reduce the emotional intervention of humans in order to make better decisions. In this course, instructor Harshit Tyagi provides a complete guide to understanding NLP using recurrent neural networks (RNNs). Harshit begins by introducing you to word encodings and using TensorFlow for tokenization. He describes the important concept of word embeddings and shows you how to use TensorFlow to classify movie reviews and project vectors. Harshit discusses RNNs and long short-term memory (LSTM), then shows you how to improve the movie review classifier from earlier in the course. He concludes with a discussion of how you can train RNNs to predict the next word in a sentence, which in turn allows you to generate some original text.

Deep Learning Foundations Modern Architectures

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Author : Tyrell Owen
language : en
Publisher: Independently Published
Release Date : 2025-12-03

Deep Learning Foundations Modern Architectures written by Tyrell Owen and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-12-03 with Computers categories.

Deep learning has entered a new era, one defined by Transformers, diffusion models, graph neural networks, and large-scale architectures that power today's breakthroughs in artificial intelligence. Whether you're a practitioner seeking mastery, a researcher advancing cutting-edge models, or an engineer deploying AI in production environments, this book gives you the deep foundations and practical frameworks needed to build the next generation of intelligent systems. Deep Learning Foundations & Modern Architectures is a complete, end-to-end guide that unifies theory, engineering, and hands-on implementation. Written for the modern AI era, it covers everything from fundamental neural network mathematics to advanced architectures used in generative AI, multimodal systems, and autonomous intelligence. This book is designed to help you understand not only how these systems work, but why they work and how to design, train, optimize, and deploy them with confidence. Inside This Book, You Will Learn How To: Build Strong Deep Learning Foundations Master Transformers and Attention-Based Models Understand and Implement Diffusion Models Work with Graph Neural Networks (GNNs) Explore Next-Generation Architectures✔ Train and Optimize Large-Scale Neural Networks Deploy and Operate Deep Learning Systems in Production Who This Book Is For Deep learning engineers AI researchers & practitioners Students learning advanced neural networks Software engineers transitioning into AI Anyone building or deploying modern AI systems Whether you're designing a new transformer variant, optimizing model training at scale, or building generative AI applications, this book provides the essential knowledge and architectural patterns you need to succeed in 2025 and beyond. Build the architectures shaping the future of AI. Deep Learning Foundations & Modern Architectures is your blueprint for mastering the models that define tomorrow's intelligent systems.

Foundations Of Machine Learning Second Edition

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Author : Mehryar Mohri
language : en
Publisher: MIT Press
Release Date : 2018-12-25

Foundations Of Machine Learning Second Edition written by Mehryar Mohri and has been published by MIT Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-12-25 with Computers categories.

A new edition of a graduate-level machine learning textbook that focuses on the analysis and theory of algorithms. This book is a general introduction to machine learning that can serve as a textbook for graduate students and a reference for researchers. It covers fundamental modern topics in machine learning while providing the theoretical basis and conceptual tools needed for the discussion and justification of algorithms. It also describes several key aspects of the application of these algorithms. The authors aim to present novel theoretical tools and concepts while giving concise proofs even for relatively advanced topics. Foundations of Machine Learning is unique in its focus on the analysis and theory of algorithms. The first four chapters lay the theoretical foundation for what follows; subsequent chapters are mostly self-contained. Topics covered include the Probably Approximately Correct (PAC) learning framework; generalization bounds based on Rademacher complexity and VC-dimension; Support Vector Machines (SVMs); kernel methods; boosting; on-line learning; multi-class classification; ranking; regression; algorithmic stability; dimensionality reduction; learning automata and languages; and reinforcement learning. Each chapter ends with a set of exercises. Appendixes provide additional material including concise probability review. This second edition offers three new chapters, on model selection, maximum entropy models, and conditional entropy models. New material in the appendixes includes a major section on Fenchel duality, expanded coverage of concentration inequalities, and an entirely new entry on information theory. More than half of the exercises are new to this edition.