Machine Learners

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Machine Learners

Author : Adrian Mackenzie
Publisher : MIT Press
ISBN 13 : 0262036827
Total Pages : 269 pages
Book Rating : 4.2/5 (62 download)

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Book Synopsis Machine Learners by : Adrian Mackenzie

Download or read book Machine Learners written by Adrian Mackenzie and published by MIT Press. This book was released on 2017-11-16 with total page 269 pages. Available in PDF, EPUB and Kindle. Book excerpt: If machine learning transforms the nature of knowledge, does it also transform the practice of critical thought? Machine learning—programming computers to learn from data—has spread across scientific disciplines, media, entertainment, and government. Medical research, autonomous vehicles, credit transaction processing, computer gaming, recommendation systems, finance, surveillance, and robotics use machine learning. Machine learning devices (sometimes understood as scientific models, sometimes as operational algorithms) anchor the field of data science. They have also become mundane mechanisms deeply embedded in a variety of systems and gadgets. In contexts from the everyday to the esoteric, machine learning is said to transform the nature of knowledge. In this book, Adrian Mackenzie investigates whether machine learning also transforms the practice of critical thinking. Mackenzie focuses on machine learners—either humans and machines or human-machine relations—situated among settings, data, and devices. The settings range from fMRI to Facebook; the data anything from cat images to DNA sequences; the devices include neural networks, support vector machines, and decision trees. He examines specific learning algorithms—writing code and writing about code—and develops an archaeology of operations that, following Foucault, views machine learning as a form of knowledge production and a strategy of power. Exploring layers of abstraction, data infrastructures, coding practices, diagrams, mathematical formalisms, and the social organization of machine learning, Mackenzie traces the mostly invisible architecture of one of the central zones of contemporary technological cultures. Mackenzie's account of machine learning locates places in which a sense of agency can take root. His archaeology of the operational formation of machine learning does not unearth the footprint of a strategic monolith but reveals the local tributaries of force that feed into the generalization and plurality of the field.

Grokking Deep Learning

Author : Andrew W. Trask
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

Introduction to Machine Learning

Author : Ethem Alpaydin
Publisher : MIT Press
ISBN 13 : 0262028182
Total Pages : 639 pages
Book Rating : 4.2/5 (62 download)

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Book Synopsis Introduction to Machine Learning by : Ethem Alpaydin

Download or read book Introduction to Machine Learning written by Ethem Alpaydin and published by MIT Press. This book was released on 2014-08-22 with total page 639 pages. Available in PDF, EPUB and Kindle. Book excerpt: Introduction -- Supervised learning -- Bayesian decision theory -- Parametric methods -- Multivariate methods -- Dimensionality reduction -- Clustering -- Nonparametric methods -- Decision trees -- Linear discrimination -- Multilayer perceptrons -- Local models -- Kernel machines -- Graphical models -- Brief contents -- Hidden markov models -- Bayesian estimation -- Combining multiple learners -- Reinforcement learning -- Design and analysis of machine learning experiments.

Programming Machine Learning

Author : Paolo Perrotta
Publisher : Pragmatic Bookshelf
ISBN 13 : 1680507710
Total Pages : 437 pages
Book Rating : 4.6/5 (85 download)

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Book Synopsis Programming Machine Learning by : Paolo Perrotta

Download or read book Programming Machine Learning written by Paolo Perrotta and published by Pragmatic Bookshelf. This book was released on 2020-03-31 with total page 437 pages. Available in PDF, EPUB and Kindle. Book excerpt: You've decided to tackle machine learning - because you're job hunting, embarking on a new project, or just think self-driving cars are cool. But where to start? It's easy to be intimidated, even as a software developer. The good news is that it doesn't have to be that hard. Master machine learning by writing code one line at a time, from simple learning programs all the way to a true deep learning system. Tackle the hard topics by breaking them down so they're easier to understand, and build your confidence by getting your hands dirty. Peel away the obscurities of machine learning, starting from scratch and going all the way to deep learning. Machine learning can be intimidating, with its reliance on math and algorithms that most programmers don't encounter in their regular work. Take a hands-on approach, writing the Python code yourself, without any libraries to obscure what's really going on. Iterate on your design, and add layers of complexity as you go. Build an image recognition application from scratch with supervised learning. Predict the future with linear regression. Dive into gradient descent, a fundamental algorithm that drives most of machine learning. Create perceptrons to classify data. Build neural networks to tackle more complex and sophisticated data sets. Train and refine those networks with backpropagation and batching. Layer the neural networks, eliminate overfitting, and add convolution to transform your neural network into a true deep learning system. Start from the beginning and code your way to machine learning mastery. What You Need: The examples in this book are written in Python, but don't worry if you don't know this language: you'll pick up all the Python you need very quickly. Apart from that, you'll only need your computer, and your code-adept brain.

Deep Learning

Author : Ian Goodfellow
Publisher : MIT Press
ISBN 13 : 0262337371
Total Pages : 801 pages
Book Rating : 4.2/5 (623 download)

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Book Synopsis Deep Learning by : Ian Goodfellow

Download or read book Deep Learning written by Ian Goodfellow and published by MIT Press. This book was released on 2016-11-10 with total page 801 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Machine Learning

Author : Ethem Alpaydin
Publisher : MIT Press
ISBN 13 : 0262529513
Total Pages : 225 pages
Book Rating : 4.2/5 (625 download)

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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.

Automated Machine Learning

Author : Frank Hutter
Publisher : Springer
ISBN 13 : 3030053180
Total Pages : 223 pages
Book Rating : 4.0/5 (3 download)

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Book Synopsis Automated Machine Learning by : Frank Hutter

Download or read book Automated Machine Learning written by Frank Hutter and published by Springer. This book was released on 2019-05-17 with total page 223 pages. Available in PDF, EPUB and Kindle. Book excerpt: This open access book presents the first comprehensive overview of general methods in Automated Machine Learning (AutoML), collects descriptions of existing systems based on these methods, and discusses the first series of international challenges of AutoML systems. The recent success of commercial ML applications and the rapid growth of the field has created a high demand for off-the-shelf ML methods that can be used easily and without expert knowledge. However, many of the recent machine learning successes crucially rely on human experts, who manually select appropriate ML architectures (deep learning architectures or more traditional ML workflows) and their hyperparameters. To overcome this problem, the field of AutoML targets a progressive automation of machine learning, based on principles from optimization and machine learning itself. This book serves as a point of entry into this quickly-developing field for researchers and advanced students alike, as well as providing a reference for practitioners aiming to use AutoML in their work.

Foundations of Machine Learning, second edition

Author : Mehryar Mohri
Publisher : MIT Press
ISBN 13 : 0262351366
Total Pages : 505 pages
Book Rating : 4.2/5 (623 download)

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Book Synopsis Foundations of Machine Learning, second edition by : Mehryar Mohri

Download or read book Foundations of Machine Learning, second edition written by Mehryar Mohri and published by MIT Press. This book was released on 2018-12-25 with total page 505 pages. Available in PDF, EPUB and Kindle. Book excerpt: A new edition of a graduate-level machine learning textbook that focuses on the analysis and theory of algorithms. This book is a general introduction to machine learning that can serve as a textbook for graduate students and a reference for researchers. It covers fundamental modern topics in machine learning while providing the theoretical basis and conceptual tools needed for the discussion and justification of algorithms. It also describes several key aspects of the application of these algorithms. The authors aim to present novel theoretical tools and concepts while giving concise proofs even for relatively advanced topics. Foundations of Machine Learning is unique in its focus on the analysis and theory of algorithms. The first four chapters lay the theoretical foundation for what follows; subsequent chapters are mostly self-contained. Topics covered include the Probably Approximately Correct (PAC) learning framework; generalization bounds based on Rademacher complexity and VC-dimension; Support Vector Machines (SVMs); kernel methods; boosting; on-line learning; multi-class classification; ranking; regression; algorithmic stability; dimensionality reduction; learning automata and languages; and reinforcement learning. Each chapter ends with a set of exercises. Appendixes provide additional material including concise probability review. This second edition offers three new chapters, on model selection, maximum entropy models, and conditional entropy models. New material in the appendixes includes a major section on Fenchel duality, expanded coverage of concentration inequalities, and an entirely new entry on information theory. More than half of the exercises are new to this edition.

Machine Learning

Author : Kevin P. Murphy
Publisher : MIT Press
ISBN 13 : 0262018020
Total Pages : 1102 pages
Book Rating : 4.2/5 (62 download)

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Book Synopsis Machine Learning by : Kevin P. Murphy

Download or read book Machine Learning written by Kevin P. Murphy and published by MIT Press. This book was released on 2012-08-24 with total page 1102 pages. Available in PDF, EPUB and Kindle. Book excerpt: A comprehensive introduction to machine learning that uses probabilistic models and inference as a unifying approach. Today's Web-enabled deluge of electronic data calls for automated methods of data analysis. Machine learning provides these, developing methods that can automatically detect patterns in data and then use the uncovered patterns to predict future data. This textbook offers a comprehensive and self-contained introduction to the field of machine learning, based on a unified, probabilistic approach. The coverage combines breadth and depth, offering necessary background material on such topics as probability, optimization, and linear algebra as well as discussion of recent developments in the field, including conditional random fields, L1 regularization, and deep learning. The book is written in an informal, accessible style, complete with pseudo-code for the most important algorithms. All topics are copiously illustrated with color images and worked examples drawn from such application domains as biology, text processing, computer vision, and robotics. Rather than providing a cookbook of different heuristic methods, the book stresses a principled model-based approach, often using the language of graphical models to specify models in a concise and intuitive way. Almost all the models described have been implemented in a MATLAB software package—PMTK (probabilistic modeling toolkit)—that is freely available online. The book is suitable for upper-level undergraduates with an introductory-level college math background and beginning graduate students.

Machine Learning

Author : Peter Flach
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.

Interpretable Machine Learning

Author : Christoph Molnar
Publisher : Lulu.com
ISBN 13 : 0244768528
Total Pages : 320 pages
Book Rating : 4.2/5 (447 download)

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Book Synopsis Interpretable Machine Learning by : Christoph Molnar

Download or read book Interpretable Machine Learning written by Christoph Molnar and published by Lulu.com. This book was released on 2020 with total page 320 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is about making machine learning models and their decisions interpretable. After exploring the concepts of interpretability, you will learn about simple, interpretable models such as decision trees, decision rules and linear regression. Later chapters focus on general model-agnostic methods for interpreting black box models like feature importance and accumulated local effects and explaining individual predictions with Shapley values and LIME. All interpretation methods are explained in depth and discussed critically. How do they work under the hood? What are their strengths and weaknesses? How can their outputs be interpreted? This book will enable you to select and correctly apply the interpretation method that is most suitable for your machine learning project.

An Introduction to Machine Learning

Author : Miroslav Kubat
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.

Proceedings of the international conference on Machine Learning

Author : John Anderson
Publisher :
ISBN 13 :
Total Pages : pages
Book Rating : 4.:/5 (632 download)

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Book Synopsis Proceedings of the international conference on Machine Learning by : John Anderson

Download or read book Proceedings of the international conference on Machine Learning written by John Anderson and published by . This book was released on 19?? with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

A First Course in Machine Learning

Author : Simon Rogers
Publisher : CRC Press
ISBN 13 : 1498738540
Total Pages : 428 pages
Book Rating : 4.4/5 (987 download)

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Book Synopsis A First Course in Machine Learning by : Simon Rogers

Download or read book A First Course in Machine Learning written by Simon Rogers and published by CRC Press. This book was released on 2016-10-14 with total page 428 pages. Available in PDF, EPUB and Kindle. Book excerpt: Introduces the main algorithms and ideas that underpin machine learning techniques and applications Keeps mathematical prerequisites to a minimum, providing mathematical explanations in comment boxes and highlighting important equations Covers modern machine learning research and techniques Includes three new chapters on Markov Chain Monte Carlo techniques, Classification and Regression with Gaussian Processes, and Dirichlet Process models Offers Python, R, and MATLAB code on accompanying website: http://www.dcs.gla.ac.uk/~srogers/firstcourseml/"

Advances in Financial Machine Learning

Author : Marcos Lopez de Prado
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.

The Hundred-page Machine Learning Book

Author : Andriy Burkov
Publisher :
ISBN 13 : 9781999579500
Total Pages : 141 pages
Book Rating : 4.5/5 (795 download)

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Book Synopsis The Hundred-page Machine Learning Book by : Andriy Burkov

Download or read book The Hundred-page Machine Learning Book written by Andriy Burkov and published by . This book was released on 2019 with total page 141 pages. Available in PDF, EPUB and Kindle. Book excerpt: Provides a practical guide to get started and execute on machine learning within a few days without necessarily knowing much about machine learning.The first five chapters are enough to get you started and the next few chapters provide you a good feel of more advanced topics to pursue.

Machine Learning

Author : Jason Bell
Publisher : John Wiley & Sons
ISBN 13 : 1119642191
Total Pages : 497 pages
Book Rating : 4.1/5 (196 download)

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Book Synopsis Machine Learning by : Jason Bell

Download or read book Machine Learning written by Jason Bell and published by John Wiley & Sons. This book was released on 2020-02-17 with total page 497 pages. Available in PDF, EPUB and Kindle. Book excerpt: Dig deep into the data with a hands-on guide to machine learning with updated examples and more! Machine Learning: Hands-On for Developers and Technical Professionals provides hands-on instruction and fully-coded working examples for the most common machine learning techniques used by developers and technical professionals. The book contains a breakdown of each ML variant, explaining how it works and how it is used within certain industries, allowing readers to incorporate the presented techniques into their own work as they follow along. A core tenant of machine learning is a strong focus on data preparation, and a full exploration of the various types of learning algorithms illustrates how the proper tools can help any developer extract information and insights from existing data. The book includes a full complement of Instructor's Materials to facilitate use in the classroom, making this resource useful for students and as a professional reference. At its core, machine learning is a mathematical, algorithm-based technology that forms the basis of historical data mining and modern big data science. Scientific analysis of big data requires a working knowledge of machine learning, which forms predictions based on known properties learned from training data. Machine Learning is an accessible, comprehensive guide for the non-mathematician, providing clear guidance that allows readers to: Learn the languages of machine learning including Hadoop, Mahout, and Weka Understand decision trees, Bayesian networks, and artificial neural networks Implement Association Rule, Real Time, and Batch learning Develop a strategic plan for safe, effective, and efficient machine learning By learning to construct a system that can learn from data, readers can increase their utility across industries. Machine learning sits at the core of deep dive data analysis and visualization, which is increasingly in demand as companies discover the goldmine hiding in their existing data. For the tech professional involved in data science, Machine Learning: Hands-On for Developers and Technical Professionals provides the skills and techniques required to dig deeper.