Understanding Machine Learning
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Understanding Machine Learning
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Author : Shai Shalev-Shwartz
language : en
Publisher: Cambridge University Press
Release Date : 2014-05-19
Understanding Machine Learning written by Shai Shalev-Shwartz and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-05-19 with Computers categories.
Introduces machine learning and its algorithmic paradigms, explaining the principles behind automated learning approaches and the considerations underlying their usage.
Understanding Machine Learning
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Author : Dr. Pinaki Ghosh
language : en
Publisher: AG PUBLISHING HOUSE (AGPH Books)
Release Date : 2023-02-14
Understanding Machine Learning written by Dr. Pinaki Ghosh and has been published by AG PUBLISHING HOUSE (AGPH Books) this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-02-14 with Study Aids categories.
Machine learning (ML) is a subfield of Al that allows computers to "self-learn" from data sets without being specifically designed for this purpose. Algorithms that use machine learning can analyse data and figure out what to expect in the future based on what they've learned. In a nutshell, algorithms and models used in machine learning acquire knowledge via repetition and experimentation. Traditional programming entails a computer engineer writing a set of instructions for a computer to follow in order to convert a given set of input data into a specified set of output data. Most commands follow an IF-THEN format: the computer does something when a given condition is satisfied. Yet, computers may learn from their own experiences and solve issues with little human intervention via a process called machine learning. Machine learning & artificial intelligence are often confused, although they are really very distinct. Machine learning is a branch of Al that allows intelligent systems to independently learn new things from data, although Al as a whole refers to robots that can make choices, acquire new skills, and solve problems in a human-like fashion. This book will explain how machine learning may be used on large datasets to provide accurate results that far beyond those of people. It may help you save time & money on analyses and duties like automating support tickets and data mining from the internal sources and the whole internet, as well as fixing consumer pain points to increase customer happiness.
Understanding Machine Understanding
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Author : Ken Clements
language : en
Publisher: Universal-Publishers
Release Date : 2024-10-15
Understanding Machine Understanding written by Ken Clements and has been published by Universal-Publishers this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-10-15 with Computers categories.
This is a comprehensive and thought-provoking exploration of the nature of machine understanding, its evaluation, and its implications. The book proposes a new framework, the Multifaceted Understanding Test Tool (MUTT), for assessing machine understanding across multiple dimensions, from language comprehension and logical reasoning to social intelligence and metacognition. Through a combination of philosophical analysis, technical exposition, and narrative thought experiments, the book delves into the frontiers of machine understanding, raising fundamental questions about the cognitive mechanisms and representations that enable genuine understanding in both human and machine minds. By probing the boundaries of artificial comprehension, the book aims to advance our theoretical grasp on the elusive notion of understanding and inform responsible development and deployment of AI technologies. In an era where Artificial Intelligence systems are becoming integral to our daily lives, a pressing question arises: Do these machines truly understand what they are doing, or are they merely sophisticated pattern matchers? "Understanding Machine Understanding" delves into this profound inquiry, exploring the depths of machine cognition and the essence of comprehension. Join Ken Clements and Claude 3 Opus on an intellectual journey that challenges conventional benchmarks like the Turing Test and introduces the innovative Multifaceted Understanding Test Tool (MUTT). This groundbreaking framework assesses AI's capabilities across language, reasoning, perception, and social intelligence, aiming to distinguish genuine understanding from mere imitation. Through philosophical analysis, technical exposition, and engaging narratives, this book invites readers to explore the frontiers of AI comprehension. Whether you're an AI researcher, philosopher, or curious observer, "Understanding Machine Understanding" offers a thought-provoking guide to the future of human-machine collaboration. Discover what it truly means for a machine to understand--and the implications for our shared future.
Machine Learning
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Author : Peter Bradley
language : en
Publisher: Machine Learning
Release Date : 2018-09-13
Machine Learning written by Peter Bradley and has been published by Machine Learning this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-09-13 with Science categories.
Do you want to know more about Machine Learning and what it means for the future?Could Machine Learning help your business to perform better?Machine Learning is not a new idea. It stems back as far as the 1950s and involves computers 'learning' from basic algorithms without the need for them to be specifically programmed to do so. If that sounds a bit like science-fiction, it isn't.Machine Learning is real and is gathering pace and in Machine Learning: A Comprehensive, Step-by-Step Guide to Learning and Understanding Machine Learning Concepts, Technology and Principles for Beginners, you can grasp what this means with chapters on: * What Machine Learning is * Basic facts about Machine Learning * Types of Machine Learning * Real life applications of Machine Learning * Artificial Intelligence * And much more...The way that technology is moving, combined with the pace of change, means that Machine Learning that is both complex and innovative will be with us very soon.That will have implications for us all, whether it is with employment, in our leisure time or in other aspects of our lives. This book serves to give you some idea of what Machine Learning will bring in the future.Get a copy today and see what's coming tomorrow!
Understanding Machine Learning
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Author : Margaret Nash
language : en
Publisher:
Release Date : 2019-06-18
Understanding Machine Learning written by Margaret Nash and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-06-18 with categories.
Machine learning is a branch of computer science concerned with the application of statistical techniques to improve performance of computer systems in the execution of specific tasks. It is significantly used when designing and programming algorithms for operation of computing tasks become ineffective, especially in the detection of network intruders, email filtering, etc. Some of the primary approaches to machine learning include decision tree learning, artificial neural network learning algorithm, deep learning and association rule learning, besides others. Applications of machine learning can extend into the domains of agriculture, bioinformatics, linguistics, marketing, economics, etc. This book elucidates the concepts and innovative models around prospective developments with respect to the field of machine learning. It aims to serve as a resource guide for students and experts alike and contribute to the growth of the discipline.
Understanding Deep Learning
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Author : Simon J.D. Prince
language : en
Publisher: MIT Press
Release Date : 2023-12-05
Understanding Deep Learning written by Simon J.D. Prince and has been published by MIT Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-12-05 with Computers categories.
An authoritative, accessible, and up-to-date treatment of deep learning that strikes a pragmatic middle ground between theory and practice. Deep learning is a fast-moving field with sweeping relevance in today’s increasingly digital world. Understanding Deep Learning provides an authoritative, accessible, and up-to-date treatment of the subject, covering all the key topics along with recent advances and cutting-edge concepts. Many deep learning texts are crowded with technical details that obscure fundamentals, but Simon Prince ruthlessly curates only the most important ideas to provide a high density of critical information in an intuitive and digestible form. From machine learning basics to advanced models, each concept is presented in lay terms and then detailed precisely in mathematical form and illustrated visually. The result is a lucid, self-contained textbook suitable for anyone with a basic background in applied mathematics. Up-to-date treatment of deep learning covers cutting-edge topics not found in existing texts, such as transformers and diffusion models Short, focused chapters progress in complexity, easing students into difficult concepts Pragmatic approach straddling theory and practice gives readers the level of detail required to implement naive versions of models Streamlined presentation separates critical ideas from background context and extraneous detail Minimal mathematical prerequisites, extensive illustrations, and practice problems make challenging material widely accessible Programming exercises offered in accompanying Python Notebooks
Understanding Machine Learning
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Author : Shai Shalev-Shwartz
language : en
Publisher:
Release Date : 2014
Understanding Machine Learning written by Shai Shalev-Shwartz and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014 with Algorithms categories.
"Machine learning is one of the fastest growing areas of computer science, with far-reaching applications. The aim of this textbook is to introduce machine learning, and the algorithmic paradigms it offers, in a principled way. The book provides an extensive theoretical account of the fundamental ideas underlying machine learning and the mathematical derivations that transform these principles into practical algorithms. Following a presentation of the basics of the field, the book covers a wide array of central topics that have not been addressed by previous textbooks. These include a discussion of the computational complexity of learning and the concepts of convexity and stability; important algorithmic paradigms including stochastic gradient descent, neural networks, and structured output learning; and emerging theoretical concepts such as the PAC-Bayes approach and compression-based bounds. Designed for an advanced undergraduate or beginning graduate course, the text makes the fundamentals and algorithms of machine learning accessible to students and non-expert readers in statistics, computer science, mathematics, and engineering"--
Introduction To Machine Learning
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Author : Aditya Tandon
language : en
Publisher: Book Bazooka
Release Date : 2019-11-28
Introduction To Machine Learning written by Aditya Tandon and has been published by Book Bazooka this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-11-28 with Computers categories.
Machine learning is one of the fastest growing areas of computer science, with far-reaching applications. The aim of this textbook is to introduce machine learning, and the algorithmic paradigms it offers, in a principled way. The book provides an extensive theoretical account of the fundamental ideas underlying machine learning and the mathematical derivations that transform these principles into practical algorithms. Following a presentation of the basics of the field, the book covers a wide array of central topics that have not been addressed by previous textbooks. These include a discussion of the computational complexity of learning and the concepts of convexity and stability; important algorithmic paradigms including stochastic gradient descent, neural networks, and structured output learning; and emerging theoretical concepts such as the PAC-Bayes approach and compression-based bounds. Designed for an advanced undergraduate or beginning graduate course, the text makes the fundamentals and algorithms of machine learning accessible to students and nonexpert readers in statistics, computer science, mathematics, and engineering.
Introduction To Mathematics For Understanding Deep Learning
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Author : Kazuyuki FUJII
language : en
Publisher: Scientific Research Publishing, Inc. USA
Release Date : 2018-08-30
Introduction To Mathematics For Understanding Deep Learning written by Kazuyuki FUJII and has been published by Scientific Research Publishing, Inc. USA this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-08-30 with Mathematics categories.
Deep Learning is the heart of Artificial Intelligence and will become a most important field in Data Science in the near future. Deep Learning has attracted much attention recently. It is usually carried out by the gradient descent method, which is not always easy to understand for beginners. When one starts studying Deep Learning first hurdles are (1) how to choose the learning rate (2) how to avoid being trapped by local minima (3) what is a deep meaning of the minibatch. In this book I plan to offer intuitive answers to these questions within my understandings. As a matter of course, when beginners study Deep Learning some mathematical knowledge from Calculus, Linear Algebra, Statistics and Information are required. In the book I gave minimum knowledge required for understanding Deep learning. After reading the book, readers are encouraged to challenge advanced books of Deep Learning (or Artificial Intelligence).
Python Machine Learning
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Author : Brandon Railey
language : en
Publisher:
Release Date : 2019-04-08
Python Machine Learning written by Brandon Railey and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-04-08 with categories.
Have you come across the terms machine learning and neural networks in most articles you have recently read? Do you also want to learn how to build a machine learning model that will answer your questions within a blink of your eyes? If you responded yes to any of the above questions, you have come to the right place. Machine learning is an incredibly dense topic. It's hard to imagine condensing it into an easily readable and digestible format. However, this book aims to do exactly that. Machine learning and artificial intelligence have been used in different machines and applications to improve the user's experience. One can also use machine learning to make data analysis and predicting the output for some data sets easy. All you need to do is choose the right algorithm, train the model and test the model before you apply it on any real-world tool. It is that simple isn't it? ★★Apart from this, you will also learn more about:★★ The Different Types Of Learning Algorithm That You Can Expect To Encounter The Numerous Applications Of Machine Learning And Deep Learning The Best Practices For Picking Up Neural Networks What Are The Best Languages And Libraries To Work With The Various Problems That You Can Solve With Machine Learning Algorithms And much more... Well, you can do it faster if you use Python. This language has made it easy for any user, even an amateur, to build a strong machine learning model since it has numerous directories and libraries that make it easy for one to build a model. Do you want to know how to build a machine learning model and a neural network? So, what are you waiting for? Grab a copy of this book now!