Teaching Machines

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Publisher : MIT Press
ISBN 13 : 026254606X
Total Pages : 325 pages
Book Rating : 4.2/5 (625 download)

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Book Synopsis Teaching Machines by : Audrey Watters

Download or read book Teaching Machines written by Audrey Watters and published by MIT Press. This book was released on 2023-02-07 with total page 325 pages. Available in PDF, EPUB and Kindle. Book excerpt: How ed tech was born: Twentieth-century teaching machines--from Sidney Pressey's mechanized test-giver to B. F. Skinner's behaviorist bell-ringing box. Contrary to popular belief, ed tech did not begin with videos on the internet. The idea of technology that would allow students to "go at their own pace" did not originate in Silicon Valley. In Teaching Machines, education writer Audrey Watters offers a lively history of predigital educational technology, from Sidney Pressey's mechanized positive-reinforcement provider to B. F. Skinner's behaviorist bell-ringing box. Watters shows that these machines and the pedagogy that accompanied them sprang from ideas--bite-sized content, individualized instruction--that had legs and were later picked up by textbook publishers and early advocates for computerized learning. Watters pays particular attention to the role of the media--newspapers, magazines, television, and film--in shaping people's perceptions of teaching machines as well as the psychological theories underpinning them. She considers these machines in the context of education reform, the political reverberations of Sputnik, and the rise of the testing and textbook industries. She chronicles Skinner's attempts to bring his teaching machines to market, culminating in the famous behaviorist's efforts to launch Didak 101, the "pre-verbal" machine that taught spelling. (Alternate names proposed by Skinner include "Autodidak," "Instructomat," and "Autostructor.") Telling these somewhat cautionary tales, Watters challenges what she calls "the teleology of ed tech"--the idea that not only is computerized education inevitable, but technological progress is the sole driver of events.

Outside in the Teaching Machine

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Author :
Publisher : Routledge
ISBN 13 : 1135070571
Total Pages : 394 pages
Book Rating : 4.1/5 (35 download)

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Book Synopsis Outside in the Teaching Machine by : Gayatri Chakravorty Spivak

Download or read book Outside in the Teaching Machine written by Gayatri Chakravorty Spivak and published by Routledge. This book was released on 2012-12-06 with total page 394 pages. Available in PDF, EPUB and Kindle. Book excerpt: Gayatri Chakravorty Spivak is one of the most pre-eminent postcolonial theorists writing today and a scholar of genuinely global reputation. This collection, first published in 1993, presents some of Spivak’s most engaging essays on works of literature such as Salman Rushdie's controversial Satanic Verses, and twentieth century thinkers such as Jacques Derrida and Karl Marx. Spivak relentlessly questions and deconstructs power structures where ever they operate. In doing so, she provides a voice for those who can not speak, proving that the true work of resistance takes place in the margins, Outside in the Teaching Machine.

Teaching Machines

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Author :
Publisher : JHU Press
ISBN 13 : 1421415402
Total Pages : 216 pages
Book Rating : 4.4/5 (214 download)

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Book Synopsis Teaching Machines by : Bill Ferster

Download or read book Teaching Machines written by Bill Ferster and published by JHU Press. This book was released on 2014-11-17 with total page 216 pages. Available in PDF, EPUB and Kindle. Book excerpt: Technology promises to make learning better, cheaper, faster—but rarely has it kept that promise. The allure of educational technology is easy to understand. Classroom instruction is an expensive and time-consuming process fraught with contradictory theories and frustratingly uneven results. Educators, inspired by machines’ contributions to modern life, have been using technology to facilitate teaching for centuries. In Teaching Machines, Bill Ferster examines past attempts to automate instruction from the earliest use of the postal service for distance education to the current maelstrom surrounding Massive Open Online Courses. He tells the stories of the entrepreneurs and visionaries who, beginning in the colonial era, developed and promoted various instructional technologies. Ferster touches on a wide range of attempts to enhance the classroom experience with machines, from hornbooks, the Chautauqua movement, and correspondence courses to B. F. Skinner’s teaching machine, intelligent tutoring systems, and eLearning. The famed progressive teachers, researchers, and administrators that the book highlights often overcame substantial hurdles to implement their ideas, but not all of them succeeded in improving the quality of education. Teaching Machines provides invaluable new insight into our current debate over the efficacy of educational technology.

Machine Learning for Kids

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Publisher : No Starch Press
ISBN 13 : 1718500572
Total Pages : 290 pages
Book Rating : 4.7/5 (185 download)

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Book Synopsis Machine Learning for Kids by : Dale Lane

Download or read book Machine Learning for Kids written by Dale Lane and published by No Starch Press. This book was released on 2021-01-19 with total page 290 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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+

Advances in Financial Machine Learning

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Author :
Publisher : John Wiley & Sons
ISBN 13 : 1119482119
Total Pages : 395 pages
Book Rating : 4.1/5 (194 download)

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Book Synopsis Advances in Financial Machine Learning by : Marcos Lopez de Prado

Download or read book Advances in Financial Machine Learning written by Marcos Lopez de Prado and published by John Wiley & Sons. This book was released on 2018-01-23 with total page 395 pages. Available in PDF, EPUB and Kindle. Book excerpt: Learn to understand and implement the latest machine learning innovations to improve your investment performance Machine learning (ML) is changing virtually every aspect of our lives. Today, ML algorithms accomplish tasks that – until recently – only expert humans could perform. And finance is ripe for disruptive innovations that will transform how the following generations understand money and invest. In the book, readers will learn how to: Structure big data in a way that is amenable to ML algorithms Conduct research with ML algorithms on big data Use supercomputing methods and back test their discoveries while avoiding false positives Advances in Financial Machine Learning addresses real life problems faced by practitioners every day, and explains scientifically sound solutions using math, supported by code and examples. Readers become active users who can test the proposed solutions in their individual setting. Written by a recognized expert and portfolio manager, this book will equip investment professionals with the groundbreaking tools needed to succeed in modern finance.

Why They Can't Write

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Publisher : JHU Press
ISBN 13 : 1421427117
Total Pages : 285 pages
Book Rating : 4.4/5 (214 download)

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Book Synopsis Why They Can't Write by : John Warner

Download or read book Why They Can't Write written by John Warner and published by JHU Press. This book was released on 2018-12-03 with total page 285 pages. Available in PDF, EPUB and Kindle. Book excerpt: An important challenge to what currently masquerades as conventional wisdom regarding the teaching of writing. There seems to be widespread agreement that—when it comes to the writing skills of college students—we are in the midst of a crisis. In Why They Can't Write, John Warner, who taught writing at the college level for two decades, argues that the problem isn't caused by a lack of rigor, or smartphones, or some generational character defect. Instead, he asserts, we're teaching writing wrong. Warner blames this on decades of educational reform rooted in standardization, assessments, and accountability. We have done no more, Warner argues, than conditioned students to perform "writing-related simulations," which pass temporary muster but do little to help students develop their writing abilities. This style of teaching has made students passive and disengaged. Worse yet, it hasn't prepared them for writing in the college classroom. Rather than making choices and thinking critically, as writers must, undergraduates simply follow the rules—such as the five-paragraph essay—designed to help them pass these high-stakes assessments. In Why They Can't Write, Warner has crafted both a diagnosis for what ails us and a blueprint for fixing a broken system. Combining current knowledge of what works in teaching and learning with the most enduring philosophies of classical education, this book challenges readers to develop the skills, attitudes, knowledge, and habits of mind of strong writers.

The Technology of Teaching

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Author :
Publisher : B. F. Skinner Foundation
ISBN 13 : 099645392X
Total Pages : 268 pages
Book Rating : 4.9/5 (964 download)

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Book Synopsis The Technology of Teaching by : B. F. Skinner

Download or read book The Technology of Teaching written by B. F. Skinner and published by B. F. Skinner Foundation. This book was released on 2016-04-26 with total page 268 pages. Available in PDF, EPUB and Kindle. Book excerpt: On Parent's Day, in 1952, B. F. Skinner visited his daughter's fourth grade math class. As he watched the lesson, he became increasingly uncomfortable. Almost every principle of effective teaching that he had studied for more than 20 years was being violated in that classroom. Yet it was a typical class. The teacher showed how to solve the day's problems, then gave the students a worksheet to do. Some children began to work readily while others shifted uncomfortably in their chairs, or raised their hands for help. The teacher went from desk to desk, giving help and feedback. Skinner knew what was needed. Each student should be given a problem tailored precisely to his or her skill level, not to the class average, and every answer needed to be assessed immediately to determine the next step. The task was clearly impossible for one teacher. That afternoon, Skinner set to work on a teaching machine. Today's computers have made the mechanical machine obsolete, but the principles of how to design instruction in steps that lead from a basic level to competent performance are as valid today as they were in the 20th century. This book brings together Skinner's writings on education during the years he was most involved in improving education.

Generative Deep Learning

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Publisher : "O'Reilly Media, Inc."
ISBN 13 : 1492041890
Total Pages : 301 pages
Book Rating : 4.4/5 (92 download)

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Book Synopsis Generative Deep Learning by : David Foster

Download or read book Generative Deep Learning written by David Foster and published by "O'Reilly Media, Inc.". This book was released on 2019-06-28 with total page 301 pages. Available in PDF, EPUB and Kindle. Book excerpt: Generative modeling is one of the hottest topics in AI. It’s now possible to teach a machine to excel at human endeavors such as painting, writing, and composing music. With this practical book, machine-learning engineers and data scientists will discover how to re-create some of the most impressive examples of generative deep learning models, such as variational autoencoders,generative adversarial networks (GANs), encoder-decoder models and world models. Author David Foster demonstrates the inner workings of each technique, starting with the basics of deep learning before advancing to some of the most cutting-edge algorithms in the field. Through tips and tricks, you’ll understand how to make your models learn more efficiently and become more creative. Discover how variational autoencoders can change facial expressions in photos Build practical GAN examples from scratch, including CycleGAN for style transfer and MuseGAN for music generation Create recurrent generative models for text generation and learn how to improve the models using attention Understand how generative models can help agents to accomplish tasks within a reinforcement learning setting Explore the architecture of the Transformer (BERT, GPT-2) and image generation models such as ProGAN and StyleGAN

Grokking Deep Learning

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Author :
Publisher : Simon and Schuster
ISBN 13 : 163835720X
Total Pages : 475 pages
Book Rating : 4.6/5 (383 download)

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Book Synopsis Grokking Deep Learning by : Andrew W. Trask

Download or read book Grokking Deep Learning written by Andrew W. Trask and published by Simon and Schuster. This book was released on 2019-01-23 with total page 475 pages. Available in PDF, EPUB and Kindle. Book excerpt: Summary Grokking Deep Learning teaches you to build deep learning neural networks from scratch! In his engaging style, seasoned deep learning expert Andrew Trask shows you the science under the hood, so you grok for yourself every detail of training neural networks. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the Technology Deep learning, a branch of artificial intelligence, teaches computers to learn by using neural networks, technology inspired by the human brain. Online text translation, self-driving cars, personalized product recommendations, and virtual voice assistants are just a few of the exciting modern advancements possible thanks to deep learning. About the Book Grokking Deep Learning teaches you to build deep learning neural networks from scratch! In his engaging style, seasoned deep learning expert Andrew Trask shows you the science under the hood, so you grok for yourself every detail of training neural networks. Using only Python and its math-supporting library, NumPy, you'll train your own neural networks to see and understand images, translate text into different languages, and even write like Shakespeare! When you're done, you'll be fully prepared to move on to mastering deep learning frameworks. What's inside The science behind deep learning Building and training your own neural networks Privacy concepts, including federated learning Tips for continuing your pursuit of deep learning About the Reader For readers with high school-level math and intermediate programming skills. About the Author Andrew Trask is a PhD student at Oxford University and a research scientist at DeepMind. Previously, Andrew was a researcher and analytics product manager at Digital Reasoning, where he trained the world's largest artificial neural network and helped guide the analytics roadmap for the Synthesys cognitive computing platform. Table of Contents Introducing deep learning: why you should learn it Fundamental concepts: how do machines learn? Introduction to neural prediction: forward propagation Introduction to neural learning: gradient descent Learning multiple weights at a time: generalizing gradient descent Building your first deep neural network: introduction to backpropagation How to picture neural networks: in your head and on paper Learning signal and ignoring noise:introduction to regularization and batching Modeling probabilities and nonlinearities: activation functions Neural learning about edges and corners: intro to convolutional neural networks Neural networks that understand language: king - man + woman == ? Neural networks that write like Shakespeare: recurrent layers for variable-length data Introducing automatic optimization: let's build a deep learning framework Learning to write like Shakespeare: long short-term memory Deep learning on unseen data: introducing federated learning Where to go from here: a brief guide

Supervised Machine Learning for Text Analysis in R

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Author :
Publisher : CRC Press
ISBN 13 : 1000461971
Total Pages : 402 pages
Book Rating : 4.0/5 (4 download)

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Book Synopsis Supervised Machine Learning for Text Analysis in R by : Emil Hvitfeldt

Download or read book Supervised Machine Learning for Text Analysis in R written by Emil Hvitfeldt and published by CRC Press. This book was released on 2021-10-22 with total page 402 pages. Available in PDF, EPUB and Kindle. Book excerpt: Text data is important for many domains, from healthcare to marketing to the digital humanities, but specialized approaches are necessary to create features for machine learning from language. Supervised Machine Learning for Text Analysis in R explains how to preprocess text data for modeling, train models, and evaluate model performance using tools from the tidyverse and tidymodels ecosystem. Models like these can be used to make predictions for new observations, to understand what natural language features or characteristics contribute to differences in the output, and more. If you are already familiar with the basics of predictive modeling, use the comprehensive, detailed examples in this book to extend your skills to the domain of natural language processing. This book provides practical guidance and directly applicable knowledge for data scientists and analysts who want to integrate unstructured text data into their modeling pipelines. Learn how to use text data for both regression and classification tasks, and how to apply more straightforward algorithms like regularized regression or support vector machines as well as deep learning approaches. Natural language must be dramatically transformed to be ready for computation, so we explore typical text preprocessing and feature engineering steps like tokenization and word embeddings from the ground up. These steps influence model results in ways we can measure, both in terms of model metrics and other tangible consequences such as how fair or appropriate model results are.

An Introduction to Machine Learning

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Author :
Publisher : Springer
ISBN 13 : 3319639137
Total Pages : 348 pages
Book Rating : 4.3/5 (196 download)

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Book Synopsis An Introduction to Machine Learning by : Miroslav Kubat

Download or read book An Introduction to Machine Learning written by Miroslav Kubat and published by Springer. This book was released on 2017-08-31 with total page 348 pages. Available in PDF, EPUB and Kindle. Book excerpt: This textbook presents fundamental machine learning concepts in an easy to understand manner by providing practical advice, using straightforward examples, and offering engaging discussions of relevant applications. The main topics include Bayesian classifiers, nearest-neighbor classifiers, linear and polynomial classifiers, decision trees, neural networks, and support vector machines. Later chapters show how to combine these simple tools by way of “boosting,” how to exploit them in more complicated domains, and how to deal with diverse advanced practical issues. One chapter is dedicated to the popular genetic algorithms. This revised edition contains three entirely new chapters on critical topics regarding the pragmatic application of machine learning in industry. The chapters examine multi-label domains, unsupervised learning and its use in deep learning, and logical approaches to induction. Numerous chapters have been expanded, and the presentation of the material has been enhanced. The book contains many new exercises, numerous solved examples, thought-provoking experiments, and computer assignments for independent work.

Teaching Machines and Programming

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Author :
Publisher :
ISBN 13 :
Total Pages : 228 pages
Book Rating : 4.:/5 (39 download)

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Book Synopsis Teaching Machines and Programming by : Kenneth Austwick

Download or read book Teaching Machines and Programming written by Kenneth Austwick and published by . This book was released on 1964 with total page 228 pages. Available in PDF, EPUB and Kindle. Book excerpt: Teaching Machines and Programming covers the significant developments in teaching machines and automated teaching, as well as the major theoretical issues and attributes involved in these procedures. After a brief introduction to teaching machine procedures, this six-chapter text goes on summarizing the industrial and military applications of teaching machines. The succeeding chapters consider the underlying theory, function, and schema of the adaptive teaching system, which are related to recognizable teaching functions performed by a human tutor. The last chapters discuss the development a.

The Flickering Mind

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Publisher : Random House
ISBN 13 : 0307432211
Total Pages : 523 pages
Book Rating : 4.3/5 (74 download)

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Book Synopsis The Flickering Mind by : Todd Oppenheimer

Download or read book The Flickering Mind written by Todd Oppenheimer and published by Random House. This book was released on 2007-12-18 with total page 523 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Flickering Mind, by National Magazine Award winner Todd Oppenheimer, is a landmark account of the failure of technology to improve our schools and a call for renewed emphasis on what really works. American education faces an unusual moment of crisis. For decades, our schools have been beaten down by a series of curriculum fads, empty crusades for reform, and stingy funding. Now education and political leaders have offered their biggest and most expensive promise ever—the miracle of computers and the Internet—at a cost of approximately $70 billion just during the decade of the 1990s. Computer technology has become so prevalent that it is transforming nearly every corner of the academic world, from our efforts to close the gap between rich and poor, to our hopes for school reform, to our basic methods of developing the human imagination. Technology is also recasting the relationships that schools strike with the business community, changing public beliefs about the demands of tomorrow’s working world, and reframing the nation’s systems for researching, testing, and evaluating achievement. All this change has led to a culture of the flickering mind, and a generation teetering between two possible futures. In one, youngsters have a chance to become confident masters of the tools of their day, to better address the problems of tomorrow. Alternatively, they can become victims of commercial novelties and narrow measures of ability, underscored by misplaced faith in standardized testing. At this point, America’s students can’t even make a fair choice. They are an increasingly distracted lot. Their ability to reason, to listen, to feel empathy, is quite literally flickering. Computers and their attendant technologies did not cause all these problems, but they are quietly accelerating them. In this authoritative and impassioned account of the state of education in America, Todd Oppenheimer shows why it does not have to be this way. Oppenheimer visited dozens of schools nationwide—public and private, urban and rural—to present the compelling tales that frame this book. He consulted with experts, read volumes of studies, and came to strong and persuasive conclusions: that the essentials of learning have been gradually forgotten and that they matter much more than the novelties of technology. He argues that every time we computerize a science class or shut down a music program to pay for new hardware, we lose sight of what our priority should be: “enlightened basics.” Broad in scope and investigative in treatment, The Flickering Mind will not only contribute to a vital public conversation about what our schools can and should be—it will define the debate.

Machine Learning

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Publisher : Cambridge University Press
ISBN 13 : 1107096391
Total Pages : 415 pages
Book Rating : 4.1/5 (7 download)

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Book Synopsis Machine Learning by : Peter Flach

Download or read book Machine Learning written by Peter Flach and published by Cambridge University Press. This book was released on 2012-09-20 with total page 415 pages. Available in PDF, EPUB and Kindle. Book excerpt: Covering all the main approaches in state-of-the-art machine learning research, this will set a new standard as an introductory textbook.

The Children's Machine

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Author :
Publisher : Basic Books
ISBN 13 : 9780465010639
Total Pages : 256 pages
Book Rating : 4.0/5 (16 download)

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Book Synopsis The Children's Machine by : Seymour Papert

Download or read book The Children's Machine written by Seymour Papert and published by Basic Books. This book was released on 1994-04-29 with total page 256 pages. Available in PDF, EPUB and Kindle. Book excerpt: In his classsic book, Mindstorms: Children, Computers, and powerful Ideas, Seymour Papert set out a vision of how computers could change school. In The Children's Machine he now looks back over a decade during which American schools acquired more than three million computers and assesses progress and resistance to progress.

Probabilistic Machine Learning

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Publisher : MIT Press
ISBN 13 : 0262369303
Total Pages : 858 pages
Book Rating : 4.2/5 (623 download)

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Book Synopsis Probabilistic Machine Learning by : Kevin P. Murphy

Download or read book Probabilistic Machine Learning written by Kevin P. Murphy and published by MIT Press. This book was released on 2022-03-01 with total page 858 pages. Available in PDF, EPUB and Kindle. Book excerpt: A detailed and up-to-date introduction to machine learning, presented through the unifying lens of probabilistic modeling and Bayesian decision theory. This book offers a detailed and up-to-date introduction to machine learning (including deep learning) through the unifying lens of probabilistic modeling and Bayesian decision theory. The book covers mathematical background (including linear algebra and optimization), basic supervised learning (including linear and logistic regression and deep neural networks), as well as more advanced topics (including transfer learning and unsupervised learning). End-of-chapter exercises allow students to apply what they have learned, and an appendix covers notation. Probabilistic Machine Learning grew out of the author’s 2012 book, Machine Learning: A Probabilistic Perspective. More than just a simple update, this is a completely new book that reflects the dramatic developments in the field since 2012, most notably deep learning. In addition, the new book is accompanied by online Python code, using libraries such as scikit-learn, JAX, PyTorch, and Tensorflow, which can be used to reproduce nearly all the figures; this code can be run inside a web browser using cloud-based notebooks, and provides a practical complement to the theoretical topics discussed in the book. This introductory text will be followed by a sequel that covers more advanced topics, taking the same probabilistic approach.

Grokking Machine Learning

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Author :
Publisher : Simon and Schuster
ISBN 13 : 1617295914
Total Pages : 510 pages
Book Rating : 4.6/5 (172 download)

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Book Synopsis Grokking Machine Learning by : Luis Serrano

Download or read book Grokking Machine Learning written by Luis Serrano and published by Simon and Schuster. This book was released on 2021-12-14 with total page 510 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.