About Machine Learning Basics

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Deep Learning

Ian Goodfellow,Yoshua Bengio,Aaron Courville
Deep Learning Book PDF

Author : Ian Goodfellow,Yoshua Bengio,Aaron Courville
Publisher : MIT Press
Page : 801 pages
File Size : 53,9 Mb
Release : 2016-11-10
Category : Computers
ISBN : 9780262337373

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Deep Learning by Ian Goodfellow,Yoshua Bengio,Aaron Courville Pdf

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.

Deep Learning with Python

Francois Chollet
Deep Learning with Python Book PDF

Author : Francois Chollet
Publisher : Simon and Schuster
Page : 597 pages
File Size : 52,7 Mb
Release : 2017-11-30
Category : Computers
ISBN : 9781638352044

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Deep Learning with Python by Francois Chollet Pdf

Summary Deep Learning with Python introduces the field of deep learning using the Python language and the powerful Keras library. Written by Keras creator and Google AI researcher François Chollet, this book builds your understanding through intuitive explanations and practical examples. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the Technology Machine learning has made remarkable progress in recent years. We went from near-unusable speech and image recognition, to near-human accuracy. We went from machines that couldn't beat a serious Go player, to defeating a world champion. Behind this progress is deep learning—a combination of engineering advances, best practices, and theory that enables a wealth of previously impossible smart applications. About the Book Deep Learning with Python introduces the field of deep learning using the Python language and the powerful Keras library. Written by Keras creator and Google AI researcher François Chollet, this book builds your understanding through intuitive explanations and practical examples. You'll explore challenging concepts and practice with applications in computer vision, natural-language processing, and generative models. By the time you finish, you'll have the knowledge and hands-on skills to apply deep learning in your own projects. What's Inside Deep learning from first principles Setting up your own deep-learning environment Image-classification models Deep learning for text and sequences Neural style transfer, text generation, and image generation About the Reader Readers need intermediate Python skills. No previous experience with Keras, TensorFlow, or machine learning is required. About the Author François Chollet works on deep learning at Google in Mountain View, CA. He is the creator of the Keras deep-learning library, as well as a contributor to the TensorFlow machine-learning framework. He also does deep-learning research, with a focus on computer vision and the application of machine learning to formal reasoning. His papers have been published at major conferences in the field, including the Conference on Computer Vision and Pattern Recognition (CVPR), the Conference and Workshop on Neural Information Processing Systems (NIPS), the International Conference on Learning Representations (ICLR), and others. Table of Contents PART 1 - FUNDAMENTALS OF DEEP LEARNING What is deep learning? Before we begin: the mathematical building blocks of neural networks Getting started with neural networks Fundamentals of machine learning PART 2 - DEEP LEARNING IN PRACTICE Deep learning for computer vision Deep learning for text and sequences Advanced deep-learning best practices Generative deep learning Conclusions appendix A - Installing Keras and its dependencies on Ubuntu appendix B - Running Jupyter notebooks on an EC2 GPU instance

Understanding Machine Learning

Shai Shalev-Shwartz,Shai Ben-David
Understanding Machine Learning Book PDF

Author : Shai Shalev-Shwartz,Shai Ben-David
Publisher : Cambridge University Press
Page : 415 pages
File Size : 52,9 Mb
Release : 2014-05-19
Category : Computers
ISBN : 9781107057135

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Understanding Machine Learning by Shai Shalev-Shwartz,Shai Ben-David Pdf

Introduces machine learning and its algorithmic paradigms, explaining the principles behind automated learning approaches and the considerations underlying their usage.

An Introduction to Machine Learning

Gopinath Rebala,Ajay Ravi,Sanjay Churiwala
An Introduction to Machine Learning Book PDF

Author : Gopinath Rebala,Ajay Ravi,Sanjay Churiwala
Publisher : Springer
Page : 263 pages
File Size : 42,6 Mb
Release : 2019-05-07
Category : Technology & Engineering
ISBN : 9783030157296

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An Introduction to Machine Learning by Gopinath Rebala,Ajay Ravi,Sanjay Churiwala Pdf

Just like electricity, Machine Learning will revolutionize our life in many ways – some of which are not even conceivable today. This book provides a thorough conceptual understanding of Machine Learning techniques and algorithms. Many of the mathematical concepts are explained in an intuitive manner. The book starts with an overview of machine learning and the underlying Mathematical and Statistical concepts before moving onto machine learning topics. It gradually builds up the depth, covering many of the present day machine learning algorithms, ending in Deep Learning and Reinforcement Learning algorithms. The book also covers some of the popular Machine Learning applications. The material in this book is agnostic to any specific programming language or hardware so that readers can try these concepts on whichever platforms they are already familiar with. Offers a comprehensive introduction to Machine Learning, while not assuming any prior knowledge of the topic; Provides a complete overview of available techniques and algorithms in conceptual terms, covering various application domains of machine learning; Not tied to any specific software language or hardware implementation.

BASICS OF ARTIFICIAL INTELLIGENCE & MACHINE LEARNING

Dr Dheeraj Mehrotra
BASICS OF ARTIFICIAL INTELLIGENCE MACHINE LEARNING Book PDF

Author : Dr Dheeraj Mehrotra
Publisher : Notion Press
Page : 78 pages
File Size : 46,6 Mb
Release : 2019-06-03
Category : Computers
ISBN : 9781645872832

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BASICS OF ARTIFICIAL INTELLIGENCE & MACHINE LEARNING by Dr Dheeraj Mehrotra Pdf

The concept of Artificial Intelligence (AI) & Machine Learning (ML) has been in practice for over years with the advent of technological progress. Over time, it has blended our lives through nearly every narration of learning, teaching, enjoyment, normal routine operations and what not. The aspect delivers a common understanding of the topics with reference to it making an impact on our lives, with a better framework of technology affecting our lives in particular. Let us look up to science for a change to be brought about in us. Let us create awareness of making technology available to people, in a broader sense. As that happens, people who are responsible need to be told about the use and misuse of the same. As we lead our lives, we come across the fact that AI, Robotics and Learning Machines seem to be the household topic of discussion. Earlier, AI was perceived to be reserved for only ‘Geniuses’ or ‘Researchers’ or the ‘computer’ community, but it very aptly integrates and impacts each and every aspect of our lives. Knowingly or unknowingly, it has become intellectually influential in shaping our thoughts, actions and the day-to-day chores.

Machine Learning For Dummies

John Paul Mueller,Luca Massaron
Machine Learning For Dummies Book PDF

Author : John Paul Mueller,Luca Massaron
Publisher : John Wiley & Sons
Page : 471 pages
File Size : 50,8 Mb
Release : 2021-02-09
Category : Computers
ISBN : 9781119724018

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Machine Learning For Dummies by John Paul Mueller,Luca Massaron Pdf

One of Mark Cuban’s top reads for better understanding A.I. (inc.com, 2021) Your comprehensive entry-level guide to machine learning While machine learning expertise doesn’t quite mean you can create your own Turing Test-proof android—as in the movie Ex Machina—it is a form of artificial intelligence and one of the most exciting technological means of identifying opportunities and solving problems fast and on a large scale. Anyone who masters the principles of machine learning is mastering a big part of our tech future and opening up incredible new directions in careers that include fraud detection, optimizing search results, serving real-time ads, credit-scoring, building accurate and sophisticated pricing models—and way, way more. Unlike most machine learning books, the fully updated 2nd Edition of Machine Learning For Dummies doesn't assume you have years of experience using programming languages such as Python (R source is also included in a downloadable form with comments and explanations), but lets you in on the ground floor, covering the entry-level materials that will get you up and running building models you need to perform practical tasks. It takes a look at the underlying—and fascinating—math principles that power machine learning but also shows that you don't need to be a math whiz to build fun new tools and apply them to your work and study. Understand the history of AI and machine learning Work with Python 3.8 and TensorFlow 2.x (and R as a download) Build and test your own models Use the latest datasets, rather than the worn out data found in other books Apply machine learning to real problems Whether you want to learn for college or to enhance your business or career performance, this friendly beginner's guide is your best introduction to machine learning, allowing you to become quickly confident using this amazing and fast-developing technology that's impacting lives for the better all over the world.

Machine Learning

Gang Chen
Machine Learning Book PDF

Author : Gang Chen
Publisher : Unknown
Page : 223 pages
File Size : 45,5 Mb
Release : 2017-01-28
Category : Electronic
ISBN : 1520477724

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Machine Learning by Gang Chen Pdf

This book is a beginner's guide to study machine learning, with focus on basic methods and algorithms. It aims at senior undergraduates, graduate students and researchers in areas, such as computer science, bioinformatics, statistics and psychology. It is helpful for readers to be familiar with elementary calculus, linear algebra and probability before understanding the concepts and contents in this book. To make it easy to understand, we also provide basic mathematic reference in Appendix A and B. Our focus is on machine learning basics, models and also the recent trends. More specifically, we provide the most widely used mathematical models, derivation and optimization techniques. We intentionally avoid the experiments and evaluations because machine learning models are sensitive to various settings and datasets. Instead, this book is more like a tutorial of machine learning methods and algorithms, with the hope that readers can understand the basics and learn how to derive equations and optimize a given objective functions. This means this book will mainly present methods and approaches to different machine learning problems. In addition, this book covers most machine learning topics, such as surprised learning, unsupervised learning and semi-supervised learning. Considering metrics playing a vital role to learn models, we start with similarity measures and build all topics based on these fundamentals. Most chapters will introduce a distinct family of machine learning models given different training inputs, with focus on understanding the models throughly. While we cannot reflect the most advances in machine learning, the mathematic methods and logics will lay solid foundations for readers to learn and handle more complex situations in different applications. We hope you enjoy and like the book.

Reinforcement Learning, second edition

Richard S. Sutton,Andrew G. Barto
Reinforcement Learning second edition Book PDF

Author : Richard S. Sutton,Andrew G. Barto
Publisher : MIT Press
Page : 549 pages
File Size : 42,5 Mb
Release : 2018-11-13
Category : Computers
ISBN : 9780262352703

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Reinforcement Learning, second edition by Richard S. Sutton,Andrew G. Barto Pdf

The significantly expanded and updated new edition of a widely used text on reinforcement learning, one of the most active research areas in artificial intelligence. Reinforcement learning, one of the most active research areas in artificial intelligence, is a computational approach to learning whereby an agent tries to maximize the total amount of reward it receives while interacting with a complex, uncertain environment. In Reinforcement Learning, Richard Sutton and Andrew Barto provide a clear and simple account of the field's key ideas and algorithms. This second edition has been significantly expanded and updated, presenting new topics and updating coverage of other topics. Like the first edition, this second edition focuses on core online learning algorithms, with the more mathematical material set off in shaded boxes. Part I covers as much of reinforcement learning as possible without going beyond the tabular case for which exact solutions can be found. Many algorithms presented in this part are new to the second edition, including UCB, Expected Sarsa, and Double Learning. Part II extends these ideas to function approximation, with new sections on such topics as artificial neural networks and the Fourier basis, and offers expanded treatment of off-policy learning and policy-gradient methods. Part III has new chapters on reinforcement learning's relationships to psychology and neuroscience, as well as an updated case-studies chapter including AlphaGo and AlphaGo Zero, Atari game playing, and IBM Watson's wagering strategy. The final chapter discusses the future societal impacts of reinforcement learning.

Basics of Linear Algebra for Machine Learning

Jason Brownlee
Basics of Linear Algebra for Machine Learning Book PDF

Author : Jason Brownlee
Publisher : Machine Learning Mastery
Page : 211 pages
File Size : 55,8 Mb
Release : 2018-01-24
Category : Computers
ISBN : 8210379456XXX

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Basics of Linear Algebra for Machine Learning by Jason Brownlee Pdf

Linear algebra is a pillar of machine learning. You cannot develop a deep understanding and application of machine learning without it. In this laser-focused Ebook, you will finally cut through the equations, Greek letters, and confusion, and discover the topics in linear algebra that you need to know. Using clear explanations, standard Python libraries, and step-by-step tutorial lessons, you will discover what linear algebra is, the importance of linear algebra to machine learning, vector, and matrix operations, matrix factorization, principal component analysis, and much more.

Machine Learning for Beginners

Scott Chesterton
Machine Learning for Beginners Book PDF

Author : Scott Chesterton
Publisher : Unknown
Page : 0 pages
File Size : 45,5 Mb
Release : 2020-10-09
Category : Electronic
ISBN : 1914052110

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Machine Learning for Beginners by Scott Chesterton Pdf

People often gets confused by words like Machine Learning, Artificial Intelligence or Deep Learning. Raise Your hand if you are among them. I'm sure that you heard several times people talking about machine learning but you only have a vague idea of what it is, isn't it? Don't worry, you are not the only one. This book is here to help those readers who want to understand machine learning in a simple language. By reading Machine Learning for beginners you will probably not become a pro in this field but you will no longer be a novice and that's for sure! With Machine Learning for beginners you will discover: The basics of Machine Learning in detail with daily life examples; The different algorithm models and computing software platforms used in Machine Learning and their practical applications; How Machine Learning applications affect in the real-world and in different fields. Interesting notes on artificial intelligence and deep learning to better understand these new crucial technologies. If you have no technical background but you are willing to get familiar with machine learning basics, don't miss this book!

Grokking Machine Learning

Luis Serrano
Grokking Machine Learning Book PDF

Author : Luis Serrano
Publisher : Simon and Schuster
Page : 510 pages
File Size : 45,5 Mb
Release : 2021-12-14
Category : Computers
ISBN : 9781617295911

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Grokking Machine Learning by Luis Serrano Pdf

Grokking Machine Learning presents machine learning algorithms and techniques in a way that anyone can understand. This book skips the confused academic jargon and offers clear explanations that require only basic algebra. As you go, you'll build interesting projects with Python, including models for spam detection and image recognition. You'll also pick up practical skills for cleaning and preparing data.

Machine Learning for Kids

Dale Lane
Machine Learning for Kids Book PDF

Author : Dale Lane
Publisher : No Starch Press
Page : 290 pages
File Size : 42,6 Mb
Release : 2021-01-19
Category : Computers
ISBN : 9781718500570

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Machine Learning for Kids by Dale Lane Pdf

A hands-on, application-based introduction to machine learning and artificial intelligence (AI) that guides young readers through creating compelling AI-powered games and applications using the Scratch programming language. Machine learning (also known as ML) is one of the building blocks of AI, or artificial intelligence. AI is based on the idea that computers can learn on their own, with your help. Machine Learning for Kids will introduce you to machine learning, painlessly. With this book and its free, Scratch-based, award-winning companion website, you'll see how easy it is to add machine learning to your own projects. You don't even need to know how to code! As you work through the book you'll discover how machine learning systems can be taught to recognize text, images, numbers, and sounds, and how to train your models to improve their accuracy. You'll turn your models into fun computer games and apps, and see what happens when they get confused by bad data. You'll build 13 projects step-by-step from the ground up, including: • Rock, Paper, Scissors game that recognizes your hand shapes • An app that recommends movies based on other movies that you like • A computer character that reacts to insults and compliments • An interactive virtual assistant (like Siri or Alexa) that obeys commands • An AI version of Pac-Man, with a smart character that knows how to avoid ghosts NOTE: This book includes a Scratch tutorial for beginners, and step-by-step instructions for every project. Ages 12+

Fundamentals of Machine Learning for Predictive Data Analytics, second edition

John D. Kelleher,Brian Mac Namee,Aoife D'Arcy
Fundamentals of Machine Learning for Predictive Data Analytics second edition Book PDF

Author : John D. Kelleher,Brian Mac Namee,Aoife D'Arcy
Publisher : MIT Press
Page : 853 pages
File Size : 54,6 Mb
Release : 2020-10-20
Category : Computers
ISBN : 9780262361101

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Fundamentals of Machine Learning for Predictive Data Analytics, second edition by John D. Kelleher,Brian Mac Namee,Aoife D'Arcy Pdf

The second edition of a comprehensive introduction to machine learning approaches used in predictive data analytics, covering both theory and practice. Machine learning is often used to build predictive models by extracting patterns from large datasets. These models are used in predictive data analytics applications including price prediction, risk assessment, predicting customer behavior, and document classification. This introductory textbook offers a detailed and focused treatment of the most important machine learning approaches used in predictive data analytics, covering both theoretical concepts and practical applications. Technical and mathematical material is augmented with explanatory worked examples, and case studies illustrate the application of these models in the broader business context. This second edition covers recent developments in machine learning, especially in a new chapter on deep learning, and two new chapters that go beyond predictive analytics to cover unsupervised learning and reinforcement learning.

Deep Learning for Coders with fastai and PyTorch

Jeremy Howard,Sylvain Gugger
Deep Learning for Coders with fastai and PyTorch Book PDF

Author : Jeremy Howard,Sylvain Gugger
Publisher : O'Reilly Media
Page : 624 pages
File Size : 52,8 Mb
Release : 2020-06-29
Category : Computers
ISBN : 9781492045496

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Deep Learning for Coders with fastai and PyTorch by Jeremy Howard,Sylvain Gugger Pdf

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

Machine Learning Basics for Beginners

Prabhakar Veeraraghavan
Machine Learning Basics for Beginners Book PDF

Author : Prabhakar Veeraraghavan
Publisher : Unknown
Page : 0 pages
File Size : 42,8 Mb
Release : 2023-11-16
Category : Electronic
ISBN : 9798223445432

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Machine Learning Basics for Beginners by Prabhakar Veeraraghavan Pdf

Machine Learning Basics for Beginners - Your comprehensive entry-level guide to machine learning Do you Want to add 'Machine Learning' skills to your LinkedIn profile? Are you ready to embark on the journey of Machine Learning from Basics? If Yes, then this book is for you! Whether you're a curious novice or a tech enthusiast eager to dive into the realm of artificial intelligence, this book is your key to understanding the fundamental concepts that power the future. This book provides the intricacies of Machine Learning with our 10 meticulously crafted chapters. Begin your journey with a warm welcome in the 'Introduction, ' paving the way for a seamless exploration of the foundational principles in 'Foundations of Machine Learning'. Navigate through the crucial terrain of 'Data Preprocessing and Feature Engineering' as you learn the essential techniques to refine and optimize your datasets. Take the leap into 'Building Your First Model, ' where you'll witness the magic unfold as you breathe life into your machine learning project. Unlock the power of predictive modeling with 'Supervised Learning Techniques, ' and then delve into the mysteries of pattern recognition with 'Unsupervised Learning Techniques.' Our step-by-step approach ensures that even the most complex concepts become clear and accessible. Venture into the future with 'Introduction to Deep Learning, ' demystifying the advanced techniques that drive cutting-edge applications, and navigate the ethical dimensions of machine learning. Wrap up your learning journey with a glimpse into 'Future Trends in Machine Learning, ' ensuring you stay ahead of the curve in this dynamic and ever-evolving field. Why This Book? Beginner-friendly language with no prior technical knowledge required. Hands-on examples and practical exercises to reinforce your learning. Insightful tips and best practices from industry experts. A holistic guide covering the entire machine learning lifecycle. Embark on a transformative learning experience with "Machine Learning Basics for Beginners" - your passport to the limitless possibilities of Machine Learning. Grab your copy now and join the ranks of those shaping the future So why wait? Order your copy today and start unlocking your full potential towards Machine Learning.