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Introduction to Machine Learning

Introduction to Machine Learning Book
Author : Ethem Alpaydin
Publisher : MIT Press
Release : 2004
ISBN : 9780262012119
Language : En, Es, Fr & De

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Book Description :

An introductory text in machine learning that gives a unified treatment of methods based on statistics, pattern recognition, neural networks, artificial intelligence, signal processing, control, and data mining.

Deep Learning

Deep Learning Book
Author : Ian Goodfellow,Yoshua Bengio,Aaron Courville
Publisher : MIT Press
Release : 2016-11-18
ISBN : 0262035618
Language : En, Es, Fr & De

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Book Description :

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

Machine Learning Book
Author : Ryszard S. Michalski,Jaime G. Carbonell,Tom M. Mitchell
Publisher : Elsevier
Release : 2014-06-28
ISBN : 008051054X
Language : En, Es, Fr & De

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Book Description :

Machine Learning: An Artificial Intelligence Approach contains tutorial overviews and research papers representative of trends in the area of machine learning as viewed from an artificial intelligence perspective. The book is organized into six parts. Part I provides an overview of machine learning and explains why machines should learn. Part II covers important issues affecting the design of learning programs—particularly programs that learn from examples. It also describes inductive learning systems. Part III deals with learning by analogy, by experimentation, and from experience. Parts IV and V discuss learning from observation and discovery, and learning from instruction, respectively. Part VI presents two studies on applied learning systems—one on the recovery of valuable information via inductive inference; the other on inducing models of simple algebraic skills from observed student performance in the context of the Leeds Modeling System (LMS). This book is intended for researchers in artificial intelligence, computer science, and cognitive psychology; students in artificial intelligence and related disciplines; and a diverse range of readers, including computer scientists, robotics experts, knowledge engineers, educators, philosophers, data analysts, psychologists, and electronic engineers.

Understanding Machine Learning

Understanding Machine Learning Book
Author : Shai Shalev-Shwartz,Shai Ben-David
Publisher : Cambridge University Press
Release : 2014-05-19
ISBN : 1107057132
Language : En, Es, Fr & De

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Book Description :

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

Lifelong Machine Learning

Lifelong Machine Learning Book
Author : Zhiyuan Chen,Bing Liu
Publisher : Morgan & Claypool Publishers
Release : 2018-08-14
ISBN : 168173303X
Language : En, Es, Fr & De

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Book Description :

Lifelong Machine Learning, Second Edition is an introduction to an advanced machine learning paradigm that continuously learns by accumulating past knowledge that it then uses in future learning and problem solving. In contrast, the current dominant machine learning paradigm learns in isolation: given a training dataset, it runs a machine learning algorithm on the dataset to produce a model that is then used in its intended application. It makes no attempt to retain the learned knowledge and use it in subsequent learning. Unlike this isolated system, humans learn effectively with only a few examples precisely because our learning is very knowledge-driven: the knowledge learned in the past helps us learn new things with little data or effort. Lifelong learning aims to emulate this capability, because without it, an AI system cannot be considered truly intelligent. Research in lifelong learning has developed significantly in the relatively short time since the first edition of this book was published. The purpose of this second edition is to expand the definition of lifelong learning, update the content of several chapters, and add a new chapter about continual learning in deep neural networks—which has been actively researched over the past two or three years. A few chapters have also been reorganized to make each of them more coherent for the reader. Moreover, the authors want to propose a unified framework for the research area. Currently, there are several research topics in machine learning that are closely related to lifelong learning—most notably, multi-task learning, transfer learning, and meta-learning—because they also employ the idea of knowledge sharing and transfer. This book brings all these topics under one roof and discusses their similarities and differences. Its goal is to introduce this emerging machine learning paradigm and present a comprehensive survey and review of the important research results and latest ideas in the area. This book is thus suitable for students, researchers, and practitioners who are interested in machine learning, data mining, natural language processing, or pattern recognition. Lecturers can readily use the book for courses in any of these related fields.

The Computational Complexity of Machine Learning

The Computational Complexity of Machine Learning Book
Author : Harvard University Center for Research in Computing Technology,Michael J. Kearns
Publisher : MIT Press
Release : 1990
ISBN : 9780262111522
Language : En, Es, Fr & De

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Book Description :

We also give algorithms for learning powerful concept classes under the uniform distribution, and give equivalences between natural models of efficient learnability. This thesis also includes detailed definitions and motivation for the distribution-free model, a chapter discussing past research in this model and related models, and a short list of important open problems."

Optimization for Machine Learning

Optimization for Machine Learning Book
Author : Suvrit Sra,Sebastian Nowozin,Stephen J. Wright
Publisher : MIT Press
Release : 2012
ISBN : 026201646X
Language : En, Es, Fr & De

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Book Description :

An up-to-date account of the interplay between optimization and machine learning, accessible to students and researchers in both communities. The interplay between optimization and machine learning is one of the most important developments in modern computational science. Optimization formulations and methods are proving to be vital in designing algorithms to extract essential knowledge from huge volumes of data. Machine learning, however, is not simply a consumer of optimization technology but a rapidly evolving field that is itself generating new optimization ideas. This book captures the state of the art of the interaction between optimization and machine learning in a way that is accessible to researchers in both fields. Optimization approaches have enjoyed prominence in machine learning because of their wide applicability and attractive theoretical properties. The increasing complexity, size, and variety of today's machine learning models call for the reassessment of existing assumptions. This book starts the process of reassessment. It describes the resurgence in novel contexts of established frameworks such as first-order methods, stochastic approximations, convex relaxations, interior-point methods, and proximal methods. It also devotes attention to newer themes such as regularized optimization, robust optimization, gradient and subgradient methods, splitting techniques, and second-order methods. Many of these techniques draw inspiration from other fields, including operations research, theoretical computer science, and subfields of optimization. The book will enrich the ongoing cross-fertilization between the machine learning community and these other fields, and within the broader optimization community.

Practical Automated Machine Learning on Azure

Practical Automated Machine Learning on Azure Book
Author : Deepak Mukunthu,Parashar Shah,Wee Hyong Tok
Publisher : O'Reilly Media
Release : 2019-09-23
ISBN : 1492055565
Language : En, Es, Fr & De

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Book Description :

Develop smart applications without spending days and weeks building machine-learning models. With this practical book, you’ll learn how to apply automated machine learning (AutoML), a process that uses machine learning to help people build machine learning models. Deepak Mukunthu, Parashar Shah, and Wee Hyong Tok provide a mix of technical depth, hands-on examples, and case studies that show how customers are solving real-world problems with this technology. Building machine-learning models is an iterative and time-consuming process. Even those who know how to create ML models may be limited in how much they can explore. Once you complete this book, you’ll understand how to apply AutoML to your data right away. Learn how companies in different industries are benefiting from AutoML Get started with AutoML using Azure Explore aspects such as algorithm selection, auto featurization, and hyperparameter tuning Understand how data analysts, BI professions, developers can use AutoML in their familiar tools and experiences Learn how to get started using AutoML for use cases including classification, regression, and forecasting.

Supervised Machine Learning in Wind Forecasting and Ramp Event Prediction

Supervised Machine Learning in Wind Forecasting and Ramp Event Prediction Book
Author : Harsh S. Dhiman,Valentina Emilia Balas
Publisher : Academic Press
Release : 2020-01-20
ISBN : 0128213531
Language : En, Es, Fr & De

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Book Description :

Supervised Machine Learning in Wind Forecasting and Ramp Event Prediction provides an up-to- date overview on the broad area of wind generation and forecasting, with a focus on the role and need of Machine Learning in this emerging field of knowledge. Various regression models and signal decomposition techniques are presented and analyzed, including least-square, twin support and random forest regression, all with supervised Machine Learning. The specific topics of ramp event prediction and wake interactions are addressed in this book, along with forecasted performance. Wind speed forecasting has become an essential component to ensure power system security, reliability and safe operation, making this reference useful for all researchers and professionals researching renewable energy, wind energy forecasting and generation. Features various supervised machine learning based regression models Offers global case studies for turbine wind farm layouts Includes state-of-the-art models and methodologies in wind forecasting

Machine Learning for IOS Developers

Machine Learning for IOS Developers Book
Author : Anonim
Publisher : Unknown
Release : 2018
ISBN : 0987650XXX
Language : En, Es, Fr & De

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Book Description :

Get started with machine learning and the Core ML framework, and learn how to build your own custom machine learning model and integrate it into an iOS app.

Foundations of Machine Learning

Foundations of Machine Learning Book
Author : Mehryar Mohri,Afshin Rostamizadeh,Ameet Talwalkar
Publisher : MIT Press
Release : 2012-08-17
ISBN : 026201825X
Language : En, Es, Fr & De

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Book Description :

Fundamental topics in machine learning are presented along with theoretical and conceptual tools for the discussion and proof of algorithms.

Machine Learning with R the tidyverse and mlr

Machine Learning with R  the tidyverse  and mlr Book
Author : Hefin I. Rhys
Publisher : Manning Publications
Release : 2020-03-31
ISBN : 1617296570
Language : En, Es, Fr & De

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Book Description :

Summary Machine learning (ML) is a collection of programming techniques for discovering relationships in data. With ML algorithms, you can cluster and classify data for tasks like making recommendations or fraud detection and make predictions for sales trends, risk analysis, and other forecasts. Once the domain of academic data scientists, machine learning has become a mainstream business process, and tools like the easy-to-learn R programming language put high-quality data analysis in the hands of any programmer. Machine Learning with R, the tidyverse, and mlr teaches you widely used ML techniques and how to apply them to your own datasets using the R programming language and its powerful ecosystem of tools. This book will get you started! Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the book Machine Learning with R, the tidyverse, and mlr gets you started in machine learning using R Studio and the awesome mlr machine learning package. This practical guide simplifies theory and avoids needlessly complicated statistics or math. All core ML techniques are clearly explained through graphics and easy-to-grasp examples. In each engaging chapter, you’ll put a new algorithm into action to solve a quirky predictive analysis problem, including Titanic survival odds, spam email filtering, and poisoned wine investigation. What's inside Using the tidyverse packages to process and plot your data Techniques for supervised and unsupervised learning Classification, regression, dimension reduction, and clustering algorithms Statistics primer to fill gaps in your knowledge About the reader For newcomers to machine learning with basic skills in R. About the author Hefin I. Rhys is a senior laboratory research scientist at the Francis Crick Institute. He runs his own YouTube channel of screencast tutorials for R and RStudio. Table of contents: PART 1 - INTRODUCTION 1.Introduction to machine learning 2. Tidying, manipulating, and plotting data with the tidyverse PART 2 - CLASSIFICATION 3. Classifying based on similarities with k-nearest neighbors 4. Classifying based on odds with logistic regression 5. Classifying by maximizing separation with discriminant analysis 6. Classifying with naive Bayes and support vector machines 7. Classifying with decision trees 8. Improving decision trees with random forests and boosting PART 3 - REGRESSION 9. Linear regression 10. Nonlinear regression with generalized additive models 11. Preventing overfitting with ridge regression, LASSO, and elastic net 12. Regression with kNN, random forest, and XGBoost PART 4 - DIMENSION REDUCTION 13. Maximizing variance with principal component analysis 14. Maximizing similarity with t-SNE and UMAP 15. Self-organizing maps and locally linear embedding PART 5 - CLUSTERING 16. Clustering by finding centers with k-means 17. Hierarchical clustering 18. Clustering based on density: DBSCAN and OPTICS 19. Clustering based on distributions with mixture modeling 20. Final notes and further reading

Linear Algebra and Optimization for Machine Learning

Linear Algebra and Optimization for Machine Learning Book
Author : Charu C. Aggarwal
Publisher : Springer Nature
Release : 2020-05-13
ISBN : 3030403440
Language : En, Es, Fr & De

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Book Description :

This textbook introduces linear algebra and optimization in the context of machine learning. Examples and exercises are provided throughout this text book together with access to a solution’s manual. This textbook targets graduate level students and professors in computer science, mathematics and data science. Advanced undergraduate students can also use this textbook. The chapters for this textbook are organized as follows: 1. Linear algebra and its applications: The chapters focus on the basics of linear algebra together with their common applications to singular value decomposition, matrix factorization, similarity matrices (kernel methods), and graph analysis. Numerous machine learning applications have been used as examples, such as spectral clustering, kernel-based classification, and outlier detection. The tight integration of linear algebra methods with examples from machine learning differentiates this book from generic volumes on linear algebra. The focus is clearly on the most relevant aspects of linear algebra for machine learning and to teach readers how to apply these concepts. 2. Optimization and its applications: Much of machine learning is posed as an optimization problem in which we try to maximize the accuracy of regression and classification models. The “parent problem” of optimization-centric machine learning is least-squares regression. Interestingly, this problem arises in both linear algebra and optimization, and is one of the key connecting problems of the two fields. Least-squares regression is also the starting point for support vector machines, logistic regression, and recommender systems. Furthermore, the methods for dimensionality reduction and matrix factorization also require the development of optimization methods. A general view of optimization in computational graphs is discussed together with its applications to back propagation in neural networks. A frequent challenge faced by beginners in machine learning is the extensive background required in linear algebra and optimization. One problem is that the existing linear algebra and optimization courses are not specific to machine learning; therefore, one would typically have to complete more course material than is necessary to pick up machine learning. Furthermore, certain types of ideas and tricks from optimization and linear algebra recur more frequently in machine learning than other application-centric settings. Therefore, there is significant value in developing a view of linear algebra and optimization that is better suited to the specific perspective of machine learning.

Machine Learning

Machine Learning Book
Author : Ryszard Stanisław Michalski,Jaime Guillermo Carbonell,Tom Michael Mitchell
Publisher : Morgan Kaufmann Pub
Release : 1983
ISBN : 0987650XXX
Language : En, Es, Fr & De

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Book Description :

Download Machine Learning book written by Ryszard Stanisław Michalski,Jaime Guillermo Carbonell,Tom Michael Mitchell, available in PDF, EPUB, and Kindle, or read full book online anywhere and anytime. Compatible with any devices.

9789386173423

9789386173423 Book
Author : Rajiv Chopra
Publisher : KHANNA PUBLISHING HOUSE
Release : 2021-05-08
ISBN : 9386173425
Language : En, Es, Fr & De

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Book Description :

This book attempts to provide a unified overview of the broad field of Machine Learning and its Practical implementation. This book is a survey of the state of art. It breaks this massive subject into comprehensible parts piece by piece. The objective is to focus on basic principles of machine learning with some leading edge topics. This book addresses a full spectrum of machine learning programming. The emphasis is to solve lot many programming examples using step-by step practical implementation of machine learning algorithms. To facilitate easy understanding of machine learning, this book has been written in such a simple style that a student thinks as if a teacher is sitting behind him and guiding him. This book is written as per the new syllabus of different Universities of India. It also Cover the syllabus of B.Tech.(CSE/IT), MCA, BCA of Delhi University, Delhi. GGSIPU, MDU, RGTU, Nagpur University, UTU, APJ Abdul Kalam University so on. The book is intended for both academic and professional audience.

Elements of Machine Learning

Elements of Machine Learning Book
Author : Pat Langley
Publisher : Morgan Kaufmann
Release : 1996
ISBN : 9781558603011
Language : En, Es, Fr & De

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Book Description :

Machine learning is the computational study of algorithms that improve performance based on experience, and this book covers the basic issues of artificial intelligence. Individual sections introduce the basic concepts and problems in machine learning, describe algorithms, discuss adaptions of the learning methods to more complex problem-solving tasks and much more.

Machine Learning for Beginners

Machine Learning for Beginners Book
Author : Harsh Bhasin
Publisher : BPB Publications
Release : 2020-08-21
ISBN : 9389845424
Language : En, Es, Fr & De

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Book Description :

Get familiar with various Supervised, Unsupervised and Reinforcement learning algorithms KEY FEATURES ● Understand the types of Machine learning. ● Get familiar with different Feature extraction methods. ● Get an overview of how Neural Network Algorithms work. ● Learn how to implement Decision Trees and Random Forests. ● The book not only explains the Classification algorithms but also discusses the deviations/ mathematical modeling. DESCRIPTION This book covers important concepts and topics in Machine Learning. It begins with Data Cleansing and presents an overview of Feature Selection. It then talks about training and testing, cross-validation, and Feature Selection. The book covers algorithms and implementations of the most common Feature Selection Techniques. The book then focuses on Linear Regression and Gradient Descent. Some of the important Classification techniques such as K-nearest neighbors, logistic regression, Naïve Bayesian, and Linear Discriminant Analysis are covered in the book. It then gives an overview of Neural Networks and explains the biological background, the limitations of the perceptron, and the backpropagation model. The Support Vector Machines and Kernel methods are also included in the book. It then shows how to implement Decision Trees and Random Forests. Towards the end, the book gives a brief overview of Unsupervised Learning. Various Feature Extraction techniques, such as Fourier Transform, STFT, and Local Binary patterns, are covered. The book also discusses Principle Component Analysis and its implementation. WHAT WILL YOU LEARN ● Learn how to prepare Data for Machine Learning. ● Learn how to implement learning algorithms from scratch. ● Use scikit-learn to implement algorithms. ● Use various Feature Selection and Feature Extraction methods. ● Learn how to develop a Face recognition system. WHO THIS BOOK IS FOR The book is designed for Undergraduate and Postgraduate Computer Science students and for the professionals who intend to switch to the fascinating world of Machine Learning. This book requires basic know-how of programming fundamentals, Python, in particular. TABLE OF CONTENTS 1. An introduction to Machine Learning 2. The beginning: Pre-Processing and Feature Selection 3. Regression 4. Classification 5. Neural Networks- I 6. Neural Networks-II 7. Support Vector machines 8. Decision Trees 9. Clustering 10. Feature Extraction Appendix A1. Cheat Sheets A2. Face Detection A3.Biblography

Machine Learning

Machine Learning Book
Author : Stephen Marsland
Publisher : CRC Press
Release : 2011-03-23
ISBN : 9781420067194
Language : En, Es, Fr & De

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Book Description :

Traditional books on machine learning can be divided into two groups- those aimed at advanced undergraduates or early postgraduates with reasonable mathematical knowledge and those that are primers on how to code algorithms. The field is ready for a text that not only demonstrates how to use the algorithms that make up machine learning methods, but

Machine Learning For Dummies

Machine Learning For Dummies Book
Author : John Paul Mueller,Luca Massaron
Publisher : John Wiley & Sons
Release : 2016-05-31
ISBN : 1119245516
Language : En, Es, Fr & De

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Book Description :

Your no-nonsense guide to making sense of machine learning Machine learning can be a mind-boggling concept for the masses, but those who are in the trenches of computer programming know just how invaluable it is. Without machine learning, fraud detection, web search results, real-time ads on web pages, credit scoring, automation, and email spam filtering wouldn't be possible, and this is only showcasing just a few of its capabilities. Written by two data science experts, Machine Learning For Dummies offers a much-needed entry point for anyone looking to use machine learning to accomplish practical tasks. Covering the entry-level topics needed to get you familiar with the basic concepts of machine learning, this guide quickly helps you make sense of the programming languages and tools you need to turn machine learning-based tasks into a reality. Whether you're maddened by the math behind machine learning, apprehensive about AI, perplexed by preprocessing data—or anything in between—this guide makes it easier to understand and implement machine learning seamlessly. Grasp how day-to-day activities are powered by machine learning Learn to 'speak' certain languages, such as Python and R, to teach machines to perform pattern-oriented tasks and data analysis Learn to code in R using R Studio Find out how to code in Python using Anaconda Dive into this complete beginner's guide so you are armed with all you need to know about machine learning!

Machine Learning in Computer Vision

Machine Learning in Computer Vision Book
Author : Nicu Sebe,Ira Cohen,Ashutosh Garg,Thomas S. Huang
Publisher : Springer Science & Business Media
Release : 2005-06-03
ISBN : 9781402032745
Language : En, Es, Fr & De

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Book Description :

The goal of this book is to address the use of several important machine learning techniques into computer vision applications. An innovative combination of computer vision and machine learning techniques has the promise of advancing the field of computer vision, which contributes to better understanding of complex real-world applications. The effective usage of machine learning technology in real-world computer vision problems requires understanding the domain of application, abstraction of a learning problem from a given computer vision task, and the selection of appropriate representations for the learnable (input) and learned (internal) entities of the system.In this book, we address all these important aspects from a new perspective: that the key element in the current computer revolution is the use of machine learning to capture the variations in visual appearance, rather than having the designer of the model accomplish this. As a bonus, models learned from large datasets are likely to be more robust and more realistic than the brittle all-design models.