Practical Mathematics For Ai And Deep Learning
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| Author | : Ronald T. Kneusel |
| Publisher | : No Starch Press |
| Total Pages | : 346 |
| Release | : 2021-12-07 |
| Genre | : Computers |
| ISBN | : 1718501900 |
Math for Deep Learning provides the essential math you need to understand deep learning discussions, explore more complex implementations, and better use the deep learning toolkits. With Math for Deep Learning, you'll learn the essential mathematics used by and as a background for deep learning. You’ll work through Python examples to learn key deep learning related topics in probability, statistics, linear algebra, differential calculus, and matrix calculus as well as how to implement data flow in a neural network, backpropagation, and gradient descent. You’ll also use Python to work through the mathematics that underlies those algorithms and even build a fully-functional neural network. In addition you’ll find coverage of gradient descent including variations commonly used by the deep learning community: SGD, Adam, RMSprop, and Adagrad/Adadelta.
| Author | : Shravan Kumar Belagal Math |
| Publisher | : |
| Total Pages | : 0 |
| Release | : 2022 |
| Genre | : Automatic control |
| ISBN | : |
This book will teach you the common terminologies used in artificial intelligence such as models, data, parameters of models, and dependent and independent variables. --
| Author | : Jay Dawani |
| Publisher | : Packt Publishing Ltd |
| Total Pages | : 347 |
| Release | : 2020-06-12 |
| Genre | : Computers |
| ISBN | : 183864184X |
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.
| Author | : Marc Peter Deisenroth |
| Publisher | : Cambridge University Press |
| Total Pages | : 392 |
| Release | : 2020-04-23 |
| Genre | : Computers |
| ISBN | : 1108569323 |
The fundamental mathematical tools needed to understand machine learning include linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability and statistics. These topics are traditionally taught in disparate courses, making it hard for data science or computer science students, or professionals, to efficiently learn the mathematics. This self-contained textbook bridges the gap between mathematical and machine learning texts, introducing the mathematical concepts with a minimum of prerequisites. It uses these concepts to derive four central machine learning methods: linear regression, principal component analysis, Gaussian mixture models and support vector machines. For students and others with a mathematical background, these derivations provide a starting point to machine learning texts. For those learning the mathematics for the first time, the methods help build intuition and practical experience with applying mathematical concepts. Every chapter includes worked examples and exercises to test understanding. Programming tutorials are offered on the book's web site.
| Author | : Tamoghna Ghosh |
| Publisher | : BPB Publications |
| Total Pages | : 572 |
| Release | : 2022-12-30 |
| Genre | : Computers |
| ISBN | : 9355511930 |
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
| Author | : Jeremy Howard |
| Publisher | : O'Reilly Media |
| Total Pages | : 624 |
| Release | : 2020-06-29 |
| Genre | : Computers |
| ISBN | : 1492045497 |
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
| Author | : Krishnendu Chaudhury |
| Publisher | : Simon and Schuster |
| Total Pages | : 550 |
| Release | : 2024-03-26 |
| Genre | : Computers |
| ISBN | : 1617296481 |
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.
| Author | : Ian Goodfellow |
| Publisher | : MIT Press |
| Total Pages | : 801 |
| Release | : 2016-11-10 |
| Genre | : Computers |
| ISBN | : 0262337371 |
An introduction to a broad range of topics in deep learning, covering mathematical and conceptual background, deep learning techniques used in industry, and research perspectives. “Written by three experts in the field, Deep Learning is the only comprehensive book on the subject.” —Elon Musk, cochair of OpenAI; cofounder and CEO of Tesla and SpaceX Deep learning is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts. Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. This book introduces a broad range of topics in deep learning. The text offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning. It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology; and it surveys such applications as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames. Finally, the book offers research perspectives, covering such theoretical topics as linear factor models, autoencoders, representation learning, structured probabilistic models, Monte Carlo methods, the partition function, approximate inference, and deep generative models. Deep Learning can be used by undergraduate or graduate students planning careers in either industry or research, and by software engineers who want to begin using deep learning in their products or platforms. A website offers supplementary material for both readers and instructors.
| Author | : Nathan Carter |
| Publisher | : American Mathematical Soc. |
| Total Pages | : 295 |
| Release | : 2021-06-08 |
| Genre | : Education |
| ISBN | : 1470464330 |
Recipient of the Mathematical Association of America's Beckenbach Book Prize in 2012! Group theory is the branch of mathematics that studies symmetry, found in crystals, art, architecture, music and many other contexts, but its beauty is lost on students when it is taught in a technical style that is difficult to understand. Visual Group Theory assumes only a high school mathematics background and covers a typical undergraduate course in group theory from a thoroughly visual perspective. The more than 300 illustrations in Visual Group Theory bring groups, subgroups, homomorphisms, products, and quotients into clear view. Every topic and theorem is accompanied with a visual demonstration of its meaning and import, from the basics of groups and subgroups through advanced structural concepts such as semidirect products and Sylow theory.
| Author | : Ryan T. White |
| Publisher | : Packt Publishing Ltd |
| Total Pages | : 330 |
| Release | : 2021-02-22 |
| Genre | : Mathematics |
| ISBN | : 1838983503 |
A practical guide simplifying discrete math for curious minds and demonstrating its application in solving problems related to software development, computer algorithms, and data science Key FeaturesApply the math of countable objects to practical problems in computer scienceExplore modern Python libraries such as scikit-learn, NumPy, and SciPy for performing mathematicsLearn complex statistical and mathematical concepts with the help of hands-on examples and expert guidanceBook Description Discrete mathematics deals with studying countable, distinct elements, and its principles are widely used in building algorithms for computer science and data science. The knowledge of discrete math concepts will help you understand the algorithms, binary, and general mathematics that sit at the core of data-driven tasks. Practical Discrete Mathematics is a comprehensive introduction for those who are new to the mathematics of countable objects. This book will help you get up to speed with using discrete math principles to take your computer science skills to a more advanced level. As you learn the language of discrete mathematics, you'll also cover methods crucial to studying and describing computer science and machine learning objects and algorithms. The chapters that follow will guide you through how memory and CPUs work. In addition to this, you'll understand how to analyze data for useful patterns, before finally exploring how to apply math concepts in network routing, web searching, and data science. By the end of this book, you'll have a deeper understanding of discrete math and its applications in computer science, and be ready to work on real-world algorithm development and machine learning. What you will learnUnderstand the terminology and methods in discrete math and their usage in algorithms and data problemsUse Boolean algebra in formal logic and elementary control structuresImplement combinatorics to measure computational complexity and manage memory allocationUse random variables, calculate descriptive statistics, and find average-case computational complexitySolve graph problems involved in routing, pathfinding, and graph searches, such as depth-first searchPerform ML tasks such as data visualization, regression, and dimensionality reductionWho this book is for This book is for computer scientists looking to expand their knowledge of discrete math, the core topic of their field. University students looking to get hands-on with computer science, mathematics, statistics, engineering, or related disciplines will also find this book useful. Basic Python programming skills and knowledge of elementary real-number algebra are required to get started with this book.
