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Mathematical Foundations For Deep Learning

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Mathematical Foundations For Deep Learning

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Author : Mehdi Ghayoumi
language : en
Publisher: CRC Press
Release Date : 2025-08-05

Mathematical Foundations For Deep Learning written by Mehdi Ghayoumi and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-08-05 with Computers categories.

Mathematical Foundations for Deep Learning bridges the gap between theoretical mathematics and practical applications in artificial intelligence (AI). This guide delves into the fundamental mathematical concepts that power modern deep learning, equipping readers with the tools and knowledge needed to excel in the rapidly evolving field of artificial intelligence. Designed for learners at all levels, from beginners to experts, the book makes mathematical ideas accessible through clear explanations, real-world examples, and targeted exercises. Readers will master core concepts in linear algebra, calculus, and optimization techniques; understand the mechanics of deep learning models; and apply theory to practice using frameworks like TensorFlow and PyTorch. By integrating theory with practical application, Mathematical Foundations for Deep Learning prepares you to navigate the complexities of AI confidently. Whether you’re aiming to develop practical skills for AI projects, advance to emerging trends in deep learning, or lay a strong foundation for future studies, this book serves as an indispensable resource for achieving proficiency in the field. Embark on an enlightening journey that fosters critical thinking and continuous learning. Invest in your future with a solid mathematical base, reinforced by case studies and applications that bring theory to life, and gain insights into the future of deep learning.

Mathematical Foundations For Deep Learning

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Author : Mehdi Ghayoumi
language : en
Publisher:
Release Date : 2025

Mathematical Foundations For Deep Learning written by Mehdi Ghayoumi and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025 with Deep learning (Machine learning) categories.

"Mathematical Foundations for Deep Learning bridges the gap between theoretical mathematics and practical applications in artificial intelligence. This guide delves into the fundamental mathematical concepts that power modern deep learning, equipping readers with the tools and knowledge needed to excel in the rapidly evolving field of AI. Designed for learners at all levels, from beginners to experts, the book makes mathematical ideas accessible through clear explanations, real-world examples, and targeted exercises. Readers will master core concepts in linear algebra, calculus, and optimization techniques, understand the mechanics of deep learning models, and apply theory to practice using frameworks like TensorFlow and PyTorch. By integrating theory with practical application, Mathematical Foundations for Deep Learning prepares you to navigate the complexities of AI confidently. Whether you're aiming to develop practical skills for AI projects, advance to emerging trends in deep learning, or lay a strong foundation for future studies, this book serves as an indispensable resource for achieving proficiency in the field. Embark on an enlightening journey that fosters critical thinking and continuous learning. Invest in your future with a solid mathematical base, reinforced by case studies and applications that bring theory to life, and gain insights into the future of deep learning"--

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.

Pro Deep Learning With Tensorflow 2 0

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Author : Santanu Pattanayak
language : en
Publisher: Apress
Release Date : 2023-01-01

Pro Deep Learning With Tensorflow 2 0 written by Santanu Pattanayak and has been published by Apress this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-01-01 with Computers categories.

This book builds upon the foundations established in its first edition, with updated chapters and the latest code implementations to bring it up to date with Tensorflow 2.0. Pro Deep Learning with TensorFlow 2.0 begins with the mathematical and core technical foundations of deep learning. Next, you will learn about convolutional neural networks, including new convolutional methods such as dilated convolution, depth-wise separable convolution, and their implementation. You’ll then gain an understanding of natural language processing in advanced network architectures such as transformers and various attention mechanisms relevant to natural language processing and neural networks in general. As you progress through the book, you’ll explore unsupervised learning frameworks that reflect the current state of deep learning methods, such as autoencoders and variational autoencoders. The final chapter covers the advanced topic of generative adversarial networks and their variants, such as cycle consistency GANs and graph neural network techniques such as graph attention networks and GraphSAGE. Upon completing this book, you will understand the mathematical foundations and concepts of deep learning, and be able to use the prototypes demonstrated to build new deep learning applications. What You Will Learn Understand full-stack deep learning using TensorFlow 2.0 Gain an understanding of the mathematical foundations of deep learning Deploy complex deep learning solutions in production using TensorFlow 2.0 Understand generative adversarial networks, graph attention networks, and GraphSAGE Who This Book Is For: Data scientists and machine learning professionals, software developers, graduate students, and open source enthusiasts.

Math And Architectures Of Deep Learning

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Author : Krishnendu Chaudhury
language : en
Publisher: Simon and Schuster
Release Date : 2024-03-26

Math And Architectures Of Deep Learning written by Krishnendu Chaudhury and has been published by Simon and Schuster this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-03-26 with Computers categories.

Math and Architectures of Deep Learning bridges the gap between theory and practice, laying out the math of deep learning side by side with practical implementations in Python and PyTorch. You'll peer inside the "black box" to understand how your code is working, and learn to comprehend cutting-edge research you can turn into practical applications. Math and Architectures of Deep Learning sets out the foundations of DL usefully and accessibly to working practitioners. Each chapter explores a new fundamental DL concept or architectural pattern, explaining the underpinning mathematics and demonstrating how they work in practice with well-annotated Python code. You'll start with a primer of basic algebra, calculus, and statistics, working your way up to state-of-the-art DL paradigms taken from the latest research. Learning mathematical foundations and neural network architecture can be challenging, but the payoff is big. You'll be free from blind reliance on pre-packaged DL models and able to build, customize, and re-architect for your specific needs. And when things go wrong, you'll be glad you can quickly identify and fix problems.

Mathematical Foundation Of Machine Learning

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Author : MADHURI SAHU (Dr.Mangala Madankar,Minakshi Ramteke,Dr.Ritesh Sule)
language : en
Publisher: Notion Press
Release Date : 2024-03-07

Mathematical Foundation Of Machine Learning written by MADHURI SAHU (Dr.Mangala Madankar,Minakshi Ramteke,Dr.Ritesh Sule) and has been published by Notion Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-03-07 with Education categories.

Embark on a transformative journey into the heart of machine intelligence with "The Essence of Learning." Authored by Madhuri Sahu, this book is a comprehensive guide for beginners and seasoned professionals, unraveling the mathematical foundations of machine learning. From linear algebra to calculus, statistics, and probability theory, the author navigates through complex algorithms, demystifying foundational concepts with real-world examples. With a focus on clarity and practicality, the book seamlessly bridges theory and application, providing readers with the tools to comprehend and implement machine learning algorithms effectively. Boasting an intuitive learning approach, practical applications, and comprehensive coverage of essential topics, this book is accessible to all levels of readers. "The Essence of Learning" equips you with the knowledge and confidence to navigate the evolving landscape of artificial intelligence, making a meaningful contribution to the expanding field of machine intelligence.

Mathematical Aspects Of Deep Learning

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Author : Philipp Grohs
language : en
Publisher: Cambridge University Press
Release Date : 2022-12-22

Mathematical Aspects Of Deep Learning written by Philipp Grohs and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-12-22 with Computers categories.

A mathematical introduction to deep learning, written by a group of leading experts in the field.

Math And Architectures Of Deep Learning

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Author : Krishnendu Chaudhury
language : en
Publisher: Simon and Schuster
Release Date : 2024-05-21

Shine a spotlight into the deep learning “black box”. This comprehensive and detailed guide reveals the mathematical and architectural concepts behind deep learning models, so you can customize, maintain, and explain them more effectively. Inside Math and Architectures of Deep Learning you will find: Math, theory, and programming principles side by side Linear algebra, vector calculus and multivariate statistics for deep learning The structure of neural networks Implementing deep learning architectures with Python and PyTorch Troubleshooting underperforming models Working code samples in downloadable Jupyter notebooks The mathematical paradigms behind deep learning models typically begin as hard-to-read academic papers that leave engineers in the dark about how those models actually function. Math and Architectures of Deep Learning bridges the gap between theory and practice, laying out the math of deep learning side by side with practical implementations in Python and PyTorch. Written by deep learning expert Krishnendu Chaudhury, you’ll peer inside the “black box” to understand how your code is working, and learn to comprehend cutting-edge research you can turn into practical applications. Foreword by Prith Banerjee. About the technology Discover what’s going on inside the black box! To work with deep learning you’ll have to choose the right model, train it, preprocess your data, evaluate performance and accuracy, and deal with uncertainty and variability in the outputs of a deployed solution. This book takes you systematically through the core mathematical concepts you’ll need as a working data scientist: vector calculus, linear algebra, and Bayesian inference, all from a deep learning perspective. About the book Math and Architectures of Deep Learning teaches the math, theory, and programming principles of deep learning models laid out side by side, and then puts them into practice with well-annotated Python code. You’ll progress from algebra, calculus, and statistics all the way to state-of-the-art DL architectures taken from the latest research. What's inside The core design principles of neural networks Implementing deep learning with Python and PyTorch Regularizing and optimizing underperforming models About the reader Readers need to know Python and the basics of algebra and calculus. About the author Krishnendu Chaudhury is co-founder and CTO of the AI startup Drishti Technologies. He previously spent a decade each at Google and Adobe. Table of Contents 1 An overview of machine learning and deep learning 2 Vectors, matrices, and tensors in machine learning 3 Classifiers and vector calculus 4 Linear algebraic tools in machine learning 5 Probability distributions in machine learning 6 Bayesian tools for machine learning 7 Function approximation: How neural networks model the world 8 Training neural networks: Forward propagation and backpropagation 9 Loss, optimization, and regularization 10 Convolutions in neural networks 11 Neural networks for image classification and object detection 12 Manifolds, homeomorphism, and neural networks 13 Fully Bayes model parameter estimation 14 Latent space and generative modeling, autoencoders, and variational autoencoders A Appendix

Practical Mathematics For Ai And Deep Learning

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Author : Tamoghna Ghosh
language : en
Publisher: BPB Publications
Release Date : 2022-12-30

Practical Mathematics For Ai And Deep Learning written by Tamoghna Ghosh and has been published by BPB Publications this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-12-30 with Computers categories.

Mathematical Codebook to Navigate Through the Fast-changing AI Landscape KEY FEATURES ● Access to industry-recognized AI methodology and deep learning mathematics with simple-to-understand examples. ● Encompasses MDP Modeling, the Bellman Equation, Auto-regressive Models, BERT, and Transformers. ● Detailed, line-by-line diagrams of algorithms, and the mathematical computations they perform. DESCRIPTION To construct a system that may be referred to as having ‘Artificial Intelligence,’ it is important to develop the capacity to design algorithms capable of performing data-based automated decision-making in conditions of uncertainty. Now, to accomplish this goal, one needs to have an in-depth understanding of the more sophisticated components of linear algebra, vector calculus, probability, and statistics. This book walks you through every mathematical algorithm, as well as its architecture, its operation, and its design so that you can understand how any artificial intelligence system operates. This book will teach you the common terminologies used in artificial intelligence such as models, data, parameters of models, and dependent and independent variables. The Bayesian linear regression, the Gaussian mixture model, the stochastic gradient descent, and the backpropagation algorithms are explored with implementation beginning from scratch. The vast majority of the sophisticated mathematics required for complicated AI computations such as autoregressive models, cycle GANs, and CNN optimization are explained and compared. You will acquire knowledge that extends beyond mathematics while reading this book. Specifically, you will become familiar with numerous AI training methods, various NLP tasks, and the process of reducing the dimensionality of data. WHAT YOU WILL LEARN ● Learn to think like a professional data scientist by picking the best-performing AI algorithms. ● Expand your mathematical horizons to include the most cutting-edge AI methods. ● Learn about Transformer Networks, improving CNN performance, dimensionality reduction, and generative models. ● Explore several neural network designs as a starting point for constructing your own NLP and Computer Vision architecture. ● Create specialized loss functions and tailor-made AI algorithms for a given business application. WHO THIS BOOK IS FOR Everyone interested in artificial intelligence and its computational foundations, including machine learning, data science, deep learning, computer vision, and natural language processing (NLP), both researchers and professionals, will find this book to be an excellent companion. This book can be useful as a quick reference for practitioners who already use a variety of mathematical topics but do not completely understand the underlying principles. TABLE OF CONTENTS 1. Overview of AI 2. Linear Algebra 3. Vector Calculus 4. Basic Statistics and Probability Theory 5. Statistics Inference and Applications 6. Neural Networks 7. Clustering 8. Dimensionality Reduction 9. Computer Vision 10. Sequence Learning Models 11. Natural Language Processing 12. Generative Models

Mathematical Aspects Of Deep Learning

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Author : Philipp Grohs
language : en
Publisher: Cambridge University Press
Release Date : 2022-12-22

In recent years the development of new classification and regression algorithms based on deep learning has led to a revolution in the fields of artificial intelligence, machine learning, and data analysis. The development of a theoretical foundation to guarantee the success of these algorithms constitutes one of the most active and exciting research topics in applied mathematics. This book presents the current mathematical understanding of deep learning methods from the point of view of the leading experts in the field. It serves both as a starting point for researchers and graduate students in computer science, mathematics, and statistics trying to get into the field and as an invaluable reference for future research.

Mathematical Foundations For Deep Learning

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