Modern Deep Learning Foundation

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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

Artificial Intelligence Machine Learning Foundations Applications Future

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Author : Dr. Surinder Kumar Bhutani
language : en
Publisher: IIP Iterative International Publishers
Release Date : 2026-01-17

Artificial Intelligence Machine Learning Foundations Applications Future written by Dr. Surinder Kumar Bhutani and has been published by IIP Iterative International Publishers this book supported file pdf, txt, epub, kindle and other format this book has been release on 2026-01-17 with Computers categories.

Artificial Intelligence (AI) and Machine Learning (ML) are no longer concepts confined to research labs or futuristic movies. They are shaping the very fabric of our daily lives, whether it is the recommendation on a streaming platform, the virtual assistant on our smartphones, the medical diagnosis aided by an algorithm, or the financial transaction flagged for fraud prevention. AI has become an invisible partner in decision-making, problem-solving, and innovation across industries. This book, Artificial Intelligence & Machine Learning: Foundations, Applications & Future, is designed to serve as a clear, structured, and practical introduction to AI and ML. It does not assume prior expertise in programming or mathematics but instead walks readers through the essential ideas, tools, and applications step by step. The aim is to make AI accessible to students, early professionals, and curious learners who want to understand how these technologies work, why they matter, and how they are shaping the world. The book is organized into five units: • Unit 1: Understanding Artificial Intelligence introduces the concept of AI, its historical evolution, and how it compares with human intelligence. It clears misconceptions and highlights why AI is one of the most transformative technologies of our time. • Unit 2: Basics of Machine Learning explains what ML is, the types of learning, the importance of data, and foundational algorithms that power real-world applications. • Unit 3: Introduction to Deep Learning explores neural networks and modern models such as CNNs, RNNs, and Transformers, which underpin breakthroughs like image recognition and natural language processing. • Unit 4: AI Tools & Practical Implementation focuses on hands-on learning with Python libraries and simple projects that readers can try to build their confidence and skills. • Unit 5: Applications, Ethics & Future examines how AI is transforming sectors like healthcare, education, agriculture, and finance while also addressing ethical challenges, societal implications, and the exciting possibilities of future AI. Each chapter balances theory with examples, updated developments, and simple explanations. To ensure readers can apply what they learn, code snippets, projects, and practical exercises are included. Additionally, curated free datasets, learning resources, and interview questions at the end of the book provide opportunities for deeper exploration and career readiness. The purpose of this book is not merely to teach AI and ML as abstract concepts but to help readers see their living presence in the world, recognize their potential, and think critically about their responsible use. I sincerely hope this book becomes a stepping stone in your journey to understanding, experimenting with, and contributing to the field of AI and Machine Learning.

Machine Learning Foundations And Applications A Practical Guide To Supervised Unsupervised And Reinforcement Learning

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Author : Jarrel E.
language : en
Publisher: Jarrel E.
Release Date : 2025-05-09

Machine Learning Foundations And Applications A Practical Guide To Supervised Unsupervised And Reinforcement Learning written by Jarrel E. and has been published by Jarrel E. this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-05-09 with Computers categories.

Master the algorithms powering today’s AI revolution. This practical guide breaks down the foundations of machine learning into clear, structured lessons—covering supervised learning, unsupervised learning, and reinforcement learning. Whether you're a student, developer, or data professional, you'll learn how real-world models like linear regression, neural networks, support vector machines, PCA, and Q-learning actually work—mathematically and computationally. This book blends theory with implementation, offering step-by-step explanations, intuitive insights, and practical tools for applying machine learning in business, research, and product development. If you're serious about learning machine learning, this is the book that takes you from first principles to advanced concepts—with clarity, depth, and purpose.

Introduction To Machine Learning Concepts And Foundations

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Author : Mrs.R.Sasikala
language : en
Publisher: SK Research Group of Companies
Release Date : 2025-11-05

Introduction To Machine Learning Concepts And Foundations written by Mrs.R.Sasikala and has been published by SK Research Group of Companies this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-11-05 with Computers categories.

Mrs.R.Sasikala, Assistant Professor, PG & Research Department of Computer Science, National College (Autonomous), Tiruchirappalli, Tamil Nadu, India. Ms.A.Malliga, Assistant Professor, PG & Research Department of Computer Science, National College (Autonomous), Tiruchirappalli, Tamil Nadu, India. Mrs.E.Fathima Novseen, Assistant Professor, PG & Research Department of Computer Science, National College (Autonomous), Tiruchirappalli, Tamil Nadu, India. Mrs.R.Vijayalakshmi, Assistant Professor, PG & Research Department of Computer Science, National College (Autonomous), Tiruchirappalli, Tamil Nadu, India.

Artificial Intelligence

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Author : Mr. T. Jones Daniel, M.Th, M.Tech, (Ph.D.)
language : en
Publisher: QUILL TECH PUBLICATIONS
Release Date : 2025-06-30

Artificial Intelligence written by Mr. T. Jones Daniel, M.Th, M.Tech, (Ph.D.) and has been published by QUILL TECH PUBLICATIONS this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-06-30 with categories.

Artificial Intelligence is a comprehensive and accessible textbook that offers a well-structured introduction to the core principles, methods, and modern advancements in the field of AI. Geared toward students, educators, and early-career researchers, the book provides a solid foundation in both theoretical concepts and practical applications across various AI domains. Beginning with the historical evolution and foundational philosophies of artificial intelligence, the book explores intelligent agents, problem-solving techniques, uninformed and informed search algorithms, and optimization strategies. It then progresses into advanced topics including machine learning, deep learning, neural networks, and natural language processing (NLP). Special emphasis is placed on real-world relevance through chapters on AI in healthcare, autonomous systems, robotics, creative industries, and ethical considerations. Contemporary innovations such as generative AI (ChatGPT, Claude, Sora), multimodal AI (GPT-4o), and autonomous agents are presented with clarity, contextual examples, and state-of-the-art insights. Designed to balance clarity and depth, the book features algorithm walkthroughs, illustrative diagrams, programming examples (including Python), and use cases spanning entertainment, education, finance, and assistive technology. Additionally, the author's social impact work—particularly around AI applications for elderly care—adds a unique humanitarian perspective. Rich with visuals, problem sets, and discussions on emerging trends like open-source AI, deepfake detection, and AI regulation, Artificial Intelligence equips readers with the knowledge and tools to critically engage with and apply AI in real-world settings.

Modern Deep Learning Foundation

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Author : Barak Or
language : en
Publisher: Independently Published
Release Date : 2025-08

Modern Deep Learning Foundation written by Barak Or and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-08 with Computers categories.

This is not just another deep learning book. Modern Deep Learning Foundations is a complete, hands-on guide for building, training, and deploying neural networks - written specifically for engineers who care about real-world systems, not just theoretical results. Dr. Barak Or is an AI researcher, entrepreneur, and educator, with a PhD in ML for navigation systems, and a professional background that spans startups, deeptech technologies, and teaching at the Google-Reichman Tech School. He holds dual degrees in aeronautical engineering and economics & management from the Technion and has trained thousands of engineers across domains. What's Inside: Clear explanations of modern architectures: CNNs, RNNs, LSTMs, Transformers, Autoencoders, and more In-depth coverage of training essentials: loss functions, backpropagation, optimization (AdamW, Lion, Adafactor), mixed precision, and regularization Practical tools for industrial use: saving and versioning models, serving with FastAPI, and deploying to the cloud with full PyTorch examples Lessons on explainability (SHAP, Grad-CAM), transfer learning, tabular data, time series, and working with real-world constraints A closing roadmap for becoming a deep learning engineer who can ship systems Each lesson is concise - filled with illustrations, examples, and engineering principles designed to build real intuition. Bonus: This book also serves as the official companion to the ArtificialGate course platform, used by enterprise teams and academic programs worldwide. All content is designed to support learners across technical backgrounds, and available in multiple languages.

Deep Learning For Coders With Fastai And Pytorch

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Author : Jeremy Howard
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2020-06-29

Deep Learning For Coders With Fastai And Pytorch written by Jeremy Howard and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-06-29 with Computers categories.

Deep learning is often viewed as the exclusive domain of math PhDs and big tech companies. But as this hands-on guide demonstrates, programmers comfortable with Python can achieve impressive results in deep learning with little math background, small amounts of data, and minimal code. How? With fastai, the first library to provide a consistent interface to the most frequently used deep learning applications. Authors Jeremy Howard and Sylvain Gugger, the creators of fastai, show you how to train a model on a wide range of tasks using fastai and PyTorch. You’ll also dive progressively further into deep learning theory to gain a complete understanding of the algorithms behind the scenes. Train models in computer vision, natural language processing, tabular data, and collaborative filtering Learn the latest deep learning techniques that matter most in practice Improve accuracy, speed, and reliability by understanding how deep learning models work Discover how to turn your models into web applications Implement deep learning algorithms from scratch Consider the ethical implications of your work Gain insight from the foreword by PyTorch cofounder, Soumith Chintala

Intelligent Business Analytics

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Author : Nitendra Kumar
language : en
Publisher: CRC Press
Release Date : 2025-08-27

Intelligent Business Analytics written by Nitendra Kumar and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-08-27 with Computers categories.

This book explores the transformative role of soft computing methods in increasing business analytics, providing a comprehensive look into how these advanced methods can be applied to complex business data for meaningful insights. Through the integration of neural network, fuzzy logic, genetic algorithms, artificial intelligence, machine learning, deep learning, and other innovative approaches, Intelligent Business Analytics: Harnessing the Power of Soft Computing for Data- Driven Insights presents a roadmap for leveraging computational intelligence in diverse areas of business decision- making. Readers will venture from predictive analytics and customer segmentation to real- time decision support systems and many other applications. Soft computing’s flexibility and applicability in the handling of uncertainty, ambiguity, and dynamic data environments shine throughout the book. Each chapter is created to be a base of theory and, at the same time, provide an applied example, so the book is appropriate for students, researchers, and professionals in the field. This book also discusses where the markets are heading and new applications that are in store for intelligent analytics to create a competitive advantage that also supports sustainable growth. At the end, this book is for those who want to learn more about using data-driven approaches and those who are ready to face the changes of the fast-evolving digital world.

Hands On Mathematics For Deep Learning

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Author : Jay Dawani
language : en
Publisher: Packt Publishing Ltd
Release Date : 2020-06-12

Hands On Mathematics For Deep Learning written by Jay Dawani and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-06-12 with Computers categories.

A comprehensive guide to getting well-versed with the mathematical techniques for building modern deep learning architectures Key FeaturesUnderstand linear algebra, calculus, gradient algorithms, and other concepts essential for training deep neural networksLearn the mathematical concepts needed to understand how deep learning models functionUse deep learning for solving problems related to vision, image, text, and sequence applicationsBook Description Most programmers and data scientists struggle with mathematics, having either overlooked or forgotten core mathematical concepts. This book uses Python libraries to help you understand the math required to build deep learning (DL) models. You'll begin by learning about core mathematical and modern computational techniques used to design and implement DL algorithms. This book will cover essential topics, such as linear algebra, eigenvalues and eigenvectors, the singular value decomposition concept, and gradient algorithms, to help you understand how to train deep neural networks. Later chapters focus on important neural networks, such as the linear neural network and multilayer perceptrons, with a primary focus on helping you learn how each model works. As you advance, you will delve into the math used for regularization, multi-layered DL, forward propagation, optimization, and backpropagation techniques to understand what it takes to build full-fledged DL models. Finally, you’ll explore CNN, recurrent neural network (RNN), and GAN models and their application. By the end of this book, you'll have built a strong foundation in neural networks and DL mathematical concepts, which will help you to confidently research and build custom models in DL. What you will learnUnderstand the key mathematical concepts for building neural network modelsDiscover core multivariable calculus conceptsImprove the performance of deep learning models using optimization techniquesCover optimization algorithms, from basic stochastic gradient descent (SGD) to the advanced Adam optimizerUnderstand computational graphs and their importance in DLExplore the backpropagation algorithm to reduce output errorCover DL algorithms such as convolutional neural networks (CNNs), sequence models, and generative adversarial networks (GANs)Who this book is for This book is for data scientists, machine learning developers, aspiring deep learning developers, or anyone who wants to understand the foundation of deep learning by learning the math behind it. Working knowledge of the Python programming language and machine learning basics is required.

Reinforcement Learning Foundations And Applications

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Author : Mukesh Kumar, Vivek Bhardwaj, Karan Bajaj, Saurav Mallik, Mingqiang Wang
language : en
Publisher: Bentham Science Publishers
Release Date : 2025-11-28

Reinforcement Learning Foundations And Applications written by Mukesh Kumar, Vivek Bhardwaj, Karan Bajaj, Saurav Mallik, Mingqiang Wang and has been published by Bentham Science Publishers this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-11-28 with Computers categories.

Reinforcement Learning: Foundations and Applications combines rigorous theory with real-world relevance to introduce readers to one of the most influential branches of modern Artificial Intelligence. Walking readers through the essential principles, algorithms, and techniques that define reinforcement learning (RL), the book highlights how RL enables intelligent systems to learn from interaction and optimize decision-making in domains such as robotics, autonomous control, game AI, finance, and healthcare. The book opens with foundational RL concepts, including Markov Decision Processes, dynamic programming, and the exploration–exploitation dilemma. It then progresses to advanced material covering policy gradient methods, actor–critic architectures, deep reinforcement learning models, and multi-agent systems. Dedicated application chapters demonstrate how RL drives adaptive control, sequential decision-making, and practical problem-solving—supported by case studies, diagrams, and algorithm pseudocode. Rich with examples, research insights, and implementation guidance, this book equips readers with both the conceptual understanding and applied perspective needed to master reinforcement learning. Key Features Blends foundational RL theory with practical, application-driven case studies. Explains both model-based and model-free reinforcement learning approaches. Covers cutting-edge methods including Deep Q-Networks, continuous control, and reward shaping. Presents clear diagrams, pseudocode, and implementation notes to support hands-on learning. Highlights current challenges, limitations, and emerging research directions in RL.