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Essential Math For Ai
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Book Synopsis Essential Math for AI by : Hala Nelson
Download or read book Essential Math for AI written by Hala Nelson and published by "O'Reilly Media, Inc.". This book was released on 2023-01-04 with total page 605 pages. Available in PDF, EPUB and Kindle. Book excerpt: Companies are scrambling to integrate AI into their systems and operations. But to build truly successful solutions, you need a firm grasp of the underlying mathematics. This accessible guide walks you through the math necessary to thrive in the AI field such as focusing on real-world applications rather than dense academic theory. Engineers, data scientists, and students alike will examine mathematical topics critical for AI--including regression, neural networks, optimization, backpropagation, convolution, Markov chains, and more--through popular applications such as computer vision, natural language processing, and automated systems. And supplementary Jupyter notebooks shed light on examples with Python code and visualizations. Whether you're just beginning your career or have years of experience, this book gives you the foundation necessary to dive deeper in the field. Understand the underlying mathematics powering AI systems, including generative adversarial networks, random graphs, large random matrices, mathematical logic, optimal control, and more Learn how to adapt mathematical methods to different applications from completely different fields Gain the mathematical fluency to interpret and explain how AI systems arrive at their decisions
Book Synopsis Essential Math for AI by : Hala Nelson
Download or read book Essential Math for AI written by Hala Nelson and published by O'Reilly Media. This book was released on 2023-01-31 with total page 425 pages. Available in PDF, EPUB and Kindle. Book excerpt: Companies are scrambling to integrate AI into their systems and operations. But to build truly successful solutions, you need a firm grasp of the underlying mathematics. This accessible guide walks you through the math necessary to thrive in the AI field such as focusing on real-world applications rather than dense academic theory. Engineers, data scientists, and students alike will examine mathematical topics critical for AI--including regression, neural networks, optimization, backpropagation, convolution, Markov chains, and more--through popular applications such as computer vision, natural language processing, and automated systems. And supplementary Jupyter notebooks shed light on examples with Python code and visualizations. Whether you're just beginning your career or have years of experience, this book gives you the foundation necessary to dive deeper in the field. Understand the underlying mathematics powering AI systems, including generative adversarial networks, random graphs, large random matrices, mathematical logic, optimal control, and more Learn how to adapt mathematical methods to different applications from completely different fields Gain the mathematical fluency to interpret and explain how AI systems arrive at their decisions
Book Synopsis Mathematics for Machine Learning by : Marc Peter Deisenroth
Download or read book Mathematics for Machine Learning written by Marc Peter Deisenroth and published by Cambridge University Press. This book was released on 2020-04-23 with total page 392 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Essential Math for AI by : Andrew Hinton
Download or read book Essential Math for AI written by Andrew Hinton and published by AI Fundamentals. This book was released on 2023-11-13 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Are you ready to unlock the mathematical secrets that power today's most advanced artificial intelligence systems? "Essential Math for AI" is an essential guide for anyone looking to understand the complex mathematical underpinnings of AI. Whether you're an AI enthusiast, a student, or a professional in the field, this book is tailored to enrich your knowledge and prepare you for the future of AI innovation. Here's what you'll discover inside: Linear Algebra: Dive into the core of machine learning with in-depth explorations of vectors, matrices, and data transformations. Probability and Statistics: Learn how to make sense of data and uncertainty, which is crucial for developing robust AI applications. Calculus: Optimize AI models using the power of derivatives, integrals, and multivariable optimization. Graph Theory: Model complex relationships and understand the algorithms that can navigate these structures in AI. Discrete Mathematics: Tackle combinatorial problems and optimize algorithmic efficiency, a cornerstone of AI development. Numerical Methods: Solve equations and approximate functions, enhancing the computational power of AI. Optimization Techniques: From gradient descent to swarm intelligence, master the methods that enhance AI performance. Game Theory: Analyze strategic decision-making and its profound implications in AI. Information Theory: Quantify and encode data, ensuring efficiency and integrity in AI systems. Topology and Geometry: Uncover hidden structures in data, paving the way for breakthroughs in AI research. "Essential Math for AI" provides a comprehensive overview of the mathematical concepts propelling AI forward and offers a glimpse into the future of how these disciplines will continue to shape the AI landscape. With chapter summaries to consolidate your learning and a clear path charted for future exploration, this book is your roadmap to becoming well-versed in the mathematics of AI. Take the next step in your AI journey. Embrace the mathematical challenges and opportunities with "Essential Math for AI."
Book Synopsis Essential Mathematics for Games and Interactive Applications by : James M. Van Verth
Download or read book Essential Mathematics for Games and Interactive Applications written by James M. Van Verth and published by CRC Press. This book was released on 2008-05-19 with total page 706 pages. Available in PDF, EPUB and Kindle. Book excerpt: Essential Mathematics for Games and Interactive Applications, 2nd edition presents the core mathematics necessary for sophisticated 3D graphics and interactive physical simulations. The book begins with linear algebra and matrix multiplication and expands on this foundation to cover such topics as color and lighting, interpolation, animation and basic game physics. Essential Mathematics focuses on the issues of 3D game development important to programmers and includes optimization guidance throughout. The new edition Windows code will now use Visual Studio.NET. There will also be DirectX support provided, along with OpenGL - due to its cross-platform nature. Programmers will find more concrete examples included in this edition, as well as additional information on tuning, optimization and robustness. The book has a companion CD-ROM with exercises and a test bank for the academic secondary market, and for main market: code examples built around a shared code base, including a math library covering all the topics presented in the book, a core vector/matrix math engine, and libraries to support basic 3D rendering and interaction.
Book Synopsis Essential Math for AI by : Hala Nelson
Download or read book Essential Math for AI written by Hala Nelson and published by "O'Reilly Media, Inc.". This book was released on 2023-01-04 with total page 1128 pages. Available in PDF, EPUB and Kindle. Book excerpt: Companies are scrambling to integrate AI into their systems and operations. But to build truly successful solutions, you need a firm grasp of the underlying mathematics. This accessible guide walks you through the math necessary to thrive in the AI field such as focusing on real-world applications rather than dense academic theory. Engineers, data scientists, and students alike will examine mathematical topics critical for AI--including regression, neural networks, optimization, backpropagation, convolution, Markov chains, and more--through popular applications such as computer vision, natural language processing, and automated systems. And supplementary Jupyter notebooks shed light on examples with Python code and visualizations. Whether you're just beginning your career or have years of experience, this book gives you the foundation necessary to dive deeper in the field. Understand the underlying mathematics powering AI systems, including generative adversarial networks, random graphs, large random matrices, mathematical logic, optimal control, and more Learn how to adapt mathematical methods to different applications from completely different fields Gain the mathematical fluency to interpret and explain how AI systems arrive at their decisions
Book Synopsis Guide to Essential Math by : Sy M. Blinder
Download or read book Guide to Essential Math written by Sy M. Blinder and published by Newnes. This book was released on 2013-02-14 with total page 285 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book reminds students in junior, senior and graduate level courses in physics, chemistry and engineering of the math they may have forgotten (or learned imperfectly) that is needed to succeed in science courses. The focus is on math actually used in physics, chemistry, and engineering, and the approach to mathematics begins with 12 examples of increasing complexity, designed to hone the student's ability to think in mathematical terms and to apply quantitative methods to scientific problems. Detailed illustrations and links to reference material online help further comprehension. The second edition features new problems and illustrations and features expanded chapters on matrix algebra and differential equations. Use of proven pedagogical techniques developed during the author’s 40 years of teaching experience New practice problems and exercises to enhance comprehension Coverage of fairly advanced topics, including vector and matrix algebra, partial differential equations, special functions and complex variables
Book Synopsis Deep Learning for Coders with fastai and PyTorch by : Jeremy Howard
Download or read book Deep Learning for Coders with fastai and PyTorch written by Jeremy Howard and published by O'Reilly Media. This book was released on 2020-06-29 with total page 624 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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
Book Synopsis Math for Deep Learning by : Ronald T. Kneusel
Download or read book Math for Deep Learning written by Ronald T. Kneusel and published by No Starch Press. This book was released on 2021-12-07 with total page 346 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Essential Math for Data Science by : Thomas Nield
Download or read book Essential Math for Data Science written by Thomas Nield and published by "O'Reilly Media, Inc.". This book was released on 2022-05-26 with total page 352 pages. Available in PDF, EPUB and Kindle. Book excerpt: Master the math needed to excel in data science, machine learning, and statistics. In this book author Thomas Nield guides you through areas like calculus, probability, linear algebra, and statistics and how they apply to techniques like linear regression, logistic regression, and neural networks. Along the way you'll also gain practical insights into the state of data science and how to use those insights to maximize your career. Learn how to: Use Python code and libraries like SymPy, NumPy, and scikit-learn to explore essential mathematical concepts like calculus, linear algebra, statistics, and machine learning Understand techniques like linear regression, logistic regression, and neural networks in plain English, with minimal mathematical notation and jargon Perform descriptive statistics and hypothesis testing on a dataset to interpret p-values and statistical significance Manipulate vectors and matrices and perform matrix decomposition Integrate and build upon incremental knowledge of calculus, probability, statistics, and linear algebra, and apply it to regression models including neural networks Navigate practically through a data science career and avoid common pitfalls, assumptions, and biases while tuning your skill set to stand out in the job market
Book Synopsis Mastering Essential Math Skills by : Richard W. Fisher
Download or read book Mastering Essential Math Skills written by Richard W. Fisher and published by . This book was released on 2003-01-15 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Provides structure and guidance to the teacher by means of speed drills, review exercises, teacher tips, word problems and new material for each day.
Download or read book The Math(s) Fix written by Conrad Wolfram and published by Wolfram Media. This book was released on 2020 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Why are we all taught maths for years of our lives? Does it really empower everyone? Or fail most and disenfranchise many? Is it crucial for the AI age or an obsolete rite of passage? The Math(s) Fix: An Education Blueprint for the AI Age is a groundbreaking book that exposes why maths education is in crisis worldwide and how the only fix is a fundamentally new mainstream subject. It argues that today's maths education is not working to elevate society with modern computation, data science and AI. Instead, students are subjugated to compete with what computers do best, and lose. This is the only book to explain why being "bad at maths" may be as much the subject's fault as the learner's: how a stuck educational ecosystem has students, parents, teachers, schools, employers and policymakers running in the wrong direction to catch up with real-world requirements. But it goes further too"¬‚¬"for the first time setting out a completely alternative vision for a core computational school subject to fix the problem and seed more general reformation of education for the AI age.
Book Synopsis Calculus Made Easy by : Silvanus P. Thompson
Download or read book Calculus Made Easy written by Silvanus P. Thompson and published by St. Martin's Press. This book was released on 2014-03-18 with total page 348 pages. Available in PDF, EPUB and Kindle. Book excerpt: Calculus Made Easy by Silvanus P. Thompson and Martin Gardner has long been the most popular calculus primer. This major revision of the classic math text makes the subject at hand still more comprehensible to readers of all levels. With a new introduction, three new chapters, modernized language and methods throughout, and an appendix of challenging and enjoyable practice problems, Calculus Made Easy has been thoroughly updated for the modern reader.
Book Synopsis Machine Learning by : Ethem Alpaydin
Download or read book Machine Learning written by Ethem Alpaydin and published by MIT Press. This book was released on 2016-10-07 with total page 225 pages. Available in PDF, EPUB and Kindle. Book excerpt: A concise overview of machine learning—computer programs that learn from data—which underlies applications that include recommendation systems, face recognition, and driverless cars. Today, machine learning underlies a range of applications we use every day, from product recommendations to voice recognition—as well as some we don't yet use everyday, including driverless cars. It is the basis of the new approach in computing where we do not write programs but collect data; the idea is to learn the algorithms for the tasks automatically from data. As computing devices grow more ubiquitous, a larger part of our lives and work is recorded digitally, and as “Big Data” has gotten bigger, the theory of machine learning—the foundation of efforts to process that data into knowledge—has also advanced. In this book, machine learning expert Ethem Alpaydin offers a concise overview of the subject for the general reader, describing its evolution, explaining important learning algorithms, and presenting example applications. Alpaydin offers an account of how digital technology advanced from number-crunching mainframes to mobile devices, putting today's machine learning boom in context. He describes the basics of machine learning and some applications; the use of machine learning algorithms for pattern recognition; artificial neural networks inspired by the human brain; algorithms that learn associations between instances, with such applications as customer segmentation and learning recommendations; and reinforcement learning, when an autonomous agent learns act so as to maximize reward and minimize penalty. Alpaydin then considers some future directions for machine learning and the new field of “data science,” and discusses the ethical and legal implications for data privacy and security.
Book Synopsis The First 100 Days of Your Book by : Joel Stafford
Download or read book The First 100 Days of Your Book written by Joel Stafford and published by . This book was released on 2019-09-04 with total page 101 pages. Available in PDF, EPUB and Kindle. Book excerpt: Today having an excellent book with an great idea isn't enough for success. Over 2,000,000 books published every year, don't expect the crowd to pick up your book and say "it is a masterwork" even if it is. I swear you won't find any marketing bullshit in this book: No "social media is the king" crap No "just order a gold marketing package" and problem is solved No "do a giveaway" or "kindle free promotion" and everybody will buy your book I collected all the working marketing steps for those who want to make an impact with their books. You won't find any of the words "strategy" or "planning" in this book. I'm a practical guy and so I try to keep the bullshit and time-wasting things away from you, but I deeply believe that there are methods that should be shared with the new authors who have limited resources to do marketing. I'm focusing mainly on KDP authors, since it is the best platform to publish indie books in 2019. You will find small steps (not time-consuming), and some bigger steps in this short book which will be effective in long term. I tried to keep these steps in a linear timeline as it may happen even in real life. I hope you will enjoy reading this book, and you will find some useful resources and unique tactics that will raise your book out from the crowd.
Book Synopsis AI Mastery Trilogy by : Andrew Hinton
Download or read book AI Mastery Trilogy written by Andrew Hinton and published by Book Bound Studios. This book was released on with total page 309 pages. Available in PDF, EPUB and Kindle. Book excerpt: Dive into the "AI Mastery Trilogy," the ultimate collection for professionals seeking to conquer the world of artificial intelligence (AI). This 3-in-1 compendium is meticulously crafted to guide you from the foundational principles of AI to the intricate mathematical frameworks and practical coding applications that will catapult your expertise to new heights. Book 1: "AI Basics for Managers" by Andrew Hinton is your gateway to understanding and implementing AI in business. It equips managers with the knowledge to navigate the AI landscape, identify opportunities, and lead their organizations toward a future of innovation and growth. Book 2: "Essential Math for AI" demystifies the mathematical backbone of AI, offering a deep dive into the core concepts that fuel AI systems. From linear algebra to game theory, this book is a treasure trove for anyone eager to grasp the numerical and logical foundations that underpin AI's transformative power. Book 3: "AI and ML for Coders" is the hands-on manual for coders ready to harness AI and machine learning in their projects. It provides a comprehensive overview of AI and ML technologies, practical coding advice, and ethical considerations, ensuring you're well-equipped to create cutting-edge, responsible AI applications. The "AI Mastery Trilogy" is more than just a set of books; it's a comprehensive learning journey designed to empower business leaders, mathematicians, and coders alike. Whether you're looking to lead, understand, or build the future of AI, this collection is an indispensable resource for mastering the art and science of one of the most exciting fields in technology. Embrace the AI revolution and secure your copy of the "AI Mastery Trilogy" today!
Book Synopsis Linear Algebra and Learning from Data by : Gilbert Strang
Download or read book Linear Algebra and Learning from Data written by Gilbert Strang and published by Wellesley-Cambridge Press. This book was released on 2019-01-31 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Linear algebra and the foundations of deep learning, together at last! From Professor Gilbert Strang, acclaimed author of Introduction to Linear Algebra, comes Linear Algebra and Learning from Data, the first textbook that teaches linear algebra together with deep learning and neural nets. This readable yet rigorous textbook contains a complete course in the linear algebra and related mathematics that students need to know to get to grips with learning from data. Included are: the four fundamental subspaces, singular value decompositions, special matrices, large matrix computation techniques, compressed sensing, probability and statistics, optimization, the architecture of neural nets, stochastic gradient descent and backpropagation.
