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The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World
Describes The Quest To Find The Master Algorithm, Which Will Take Machine Learning To The Next Level, Allowing Computers To Learn How To Solve Not Just Particular Problems But Any Problem,--novelist. ...Algorithms Increasingly Run Our Lives. They Work By Learning From The Trails Of Data We Leave In Our Newly Digital World; They Observe Us, Imitate, And Experiment. And In The World's Top Research Labs And Universities, The Race Is On To Invent The Ultimate Learning Algorithm: One Capable Of Discovering Any Knowledge From Data, And Doing Anything We Want, Before We Even Ask. Domingos Gives Us A Peek Inside The Learning Machines That Power Google, Amazon, And Your Smartphone, And Discusses What It Means For You, And For The Future Of Business, Science, And Society. The Machine Learning Revolution -- The Master Algorithm -- Hume's Problem Of Induction -- How Does Your Brain Learn? -- Evolution : Nature's Learning Algorithm -- In The Church Of The Reverend Bayes -- You Are What You Resemble -- Learning Without A Teacher -- The Pieces Of The Puzzle Fall Into Place -- This Is The World On Machine Learning. Pedro Domingos. Includes Bibliographical References And Index.
Hands-On Machine Learning with Scikit-Learn and Tensorflow: Concepts, Tools, and Techniques to Build Intelligent Systems
Through A Series Of Recent Breakthroughs, Deep Learning Has Boosted The Entire Field Of Machine Learning. Now, Even Programmers Who Know Close To Nothing About This Technology Can Use Simple, Efficien...t Tools To Implement Programs Capable Of Learning From Data. This Practical Book Shows You How. The Fundamentals Of Machine Learning. The Machine Learning Landscape ; End-to-end Machine Learning Project ; Classification ; Training Models ; Support Vector Machines ; Decision Trees ; Ensemble Learning And Random Forests ; Dimensionality Reduction -- Neural Networks And Deep Learning. Up And Running With Tensorflow ; Introduction To Artificial Neural Networks ; Training Deep Neural Nets ; Distributing Tensorflow Across Devices And Servers ; Convolutional Neural Networks ; Recurrent Neural Networks ; Autoencoders ; Reinforcement Learning -- Exercise Solutions -- Machine Learning Project Checklist -- Svm Dual Problem -- Autodiff -- Other Popular Ann Architectures. Aurélien Géron. Includes Index.
This exciting addition to the McGraw-Hill Series in Computer Science focuses on the concepts and techniques that contribute to the rapidly changing field of machine learning—including probability and ...statistics, artificial intelligence, and neural networks—unifying them all in a logical and coherent manner. Machine Learning serves as a useful reference tool for software developers and researchers, as well as an outstanding text for college students.
Data Mining: Practical Machine Learning Tools and Techniques
As with any burgeoning technology that enjoys commercial attention, the use of data mining is surrounded by a great deal of hype. Exaggerated reports tell of secrets that can be uncovered by setting a...lgorithms loose on oceans of data. But there is no magic in machine learning, no hidden power, no alchemy. Instead there is an identifiable body of practical techniques that can extract useful information from raw data. This book describes these techniques and shows how they work. The book is a major revision of the first edition that appeared in 1999. While the basic core remains the same, it has been updated to reflect the changes that have taken place over five years, and now has nearly double the references. The highlights for the new edition include thirty new technique sections; an enhanced Weka machine learning workbench, which now features an interactive interface; comprehensive information on neural networks; a new section on Bayesian networks; plus much more.* Algorithmic methods at the heart of successful data mining—including tried and true techniques as well as leading edge methods* Performance improvement techniques that work by transforming the input or output* Downloadable Weka, a collection of machine learning algorithms for data mining tasks, including tools for data pre-processing, classification, regression, clustering, association rules, and visualization—in a new, interactive interface
A Concise Overview Of Machine Learning -- Computer Programs That Learn From Data -- Which Underlies Applications That Include Recommendation Systems, Face-recognition, And Driverless Cars. Why We Are ...Interested In Machine Learning -- Machine Learning, Statistics And Data Analytics -- Pattern Recognition -- Neural Networks And Deep Learning -- Learning Clusters And Recommendations -- Learning To Take Actions -- Where Do We Go From Here? Ethem Alpaydin. Includes Bibliographical References And Index.
Python Machine Learning
Unlock deeper insights into Machine Leaning with this vital guide to cutting-edge predictive analyticsAbout This Book Leverage Python's most powerful open-source libraries for deep learning, data wran...gling, and data visualization Learn effective strategies and best practices to improve and optimize machine learning systems and algorithms Ask – and answer – tough questions of your data with robust statistical models, built for a range of datasets Who This Book Is ForIf you want to find out how to use Python to start answering critical questions of your data, pick up Python Machine Learning – whether you want to get started from scratch or want to extend your data science knowledge, this is an essential and unmissable resource.What You Will Learn Explore how to use different machine learning models to ask different questions of your data Learn how to build neural networks using Keras and Theano Find out how to write clean and elegant Python code that will optimize the strength of your algorithms Discover how to embed your machine learning model in a web application for increased accessibility Predict continuous target outcomes using regression analysis Uncover hidden patterns and structures in data with clustering Organize data using effective pre-processing techniques Get to grips with sentiment analysis to delve deeper into textual and social media data In DetailMachine learning and predictive analytics are transforming the way businesses and other organizations operate. Being able to understand trends and patterns in complex data is critical to success, becoming one of the key strategies for unlocking growth in a challenging contemporary marketplace. Python can help you deliver key insights into your data – its unique capabilities as a language let you build sophisticated algorithms and statistical models that can reveal new perspectives and answer key questions that are vital for success.Python Machine Learning gives you access to the world of predictive analytics and demonstrates why Python is one of the world's leading data science languages. If you want to ask better questions of data, or need to improve and extend the capabilities of your machine learning systems, this practical data science book is invaluable. Covering a wide range of powerful Python libraries, including scikit-learn, Theano, and Keras, and featuring guidance and tips on everything from sentiment analysis to neural networks, you'll soon be able to answer some of the most important questions facing you and your organization.Style and approachPython Machine Learning connects the fundamental theoretical principles behind machine learning to their practical application in a way that focuses you on asking and answering the right questions. It walks you through the key elements of Python and its powerful machine learning libraries, while demonstrating how to get to grips with a range of statistical models.
The Hundred-Page Machine Learning Book
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Machine Learning: A Probabilistic Perspective
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 Nec...essary 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--back Cover. Probability -- Generative Models For Discrete Data -- Gaussian Models -- Bayesian Statistics -- Frequentist Statistics -- Linear Regression -- Logistic Regression -- Generalized Linear Models And The Exponential Family -- Directed Graphical Models (bayes Nets) -- Mixture Models And The Em Algorithm -- Latent Linear Models -- Sparse Linear Models -- Kernels -- Gaussian Processes -- Adaptive Basis Function Models -- Markov And Hidden Markov Models -- State Space Models -- Undirected Graphical Models (markov Random Fields) -- Exact Inference For Graphical Models -- Variational Inference -- More Variational Inference -- Monte Carlo Inference -- Markov Chain Monte Carlo (mcmc) Inference -- Clustering -- Graphical Model Structure Learning -- Latent Variable Models For Discrete Data -- Deep Learning -- Notation. Kevin P. Murphy. Includes Bibliographical References And Index.
Machine Learning for Hackers
If you&#8217;re an experienced programmer interested in crunching data, this book will get you started with machine learning&#8212;a toolkit of algorithms that enables computers to train thems...elves to automate useful tasks. Authors Drew Conway and John Myles White help you understand machine learning and statistics tools through a series of hands-on case studies, instead of a traditional math-heavy presentation.Each chapter focuses on a specific problem in machine learning, such as classification, prediction, optimization, and recommendation. Using the R programming language, you&#8217;ll learn how to analyze sample datasets and write simple machine learning algorithms. Machine Learning for Hackers is ideal for programmers from any background, including business, government, and academic research.Develop a na&#239;ve Bayesian classifier to determine if an email is spam, based only on its textUse linear regression to predict the number of page views for the top 1,000 websitesLearn optimization techniques by attempting to break a simple letter cipherCompare and contrast U.S. Senators statistically, based on their voting recordsBuild a &#8220;whom to follow&#8221; recommendation system from Twitter data
Introduction to Machine Learning with Python: A Guide for Data Scientists
Andreas C. Müller and Sarah Guido. Includes index.