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|From Statistical Physics to Statistical Inference and Back (Nato Science Series C:)
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Physicists, when modelling physical systems with a large number of degrees of freedom, and statisticians, when performing data analysis, have developed their own concepts and methods for making the `best' inference. But are these methods equivalent, or not? What is the state of the art in making inferences? The physicists want answers. More: neural computation demands a clearer understanding of how neural systems make inferences; the theory of chaotic nonlinear systems as applied to time series analysis could profit from the experience already booked by the statisticians; and finally, there is a long-standing conjecture that some of the puzzles of quantum mechanics are due to our incomplete understanding of how we make inferences. Matter enough to stimulate the writing of such a book as the present one.
But other considerations also arise, such as the maximum entropy method and Bayesian inference, information theory and the minimum description length. Finally, it is pointed out that an understanding of human inference may require input from psychologists. This lively debate, which is of acute current interest, is well summarized in the present work.
- From Statistical Physics to Statistical Inference and Back Nato Science Series C
|Machine Learning For Dummies
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Author: John Paul Mueller
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!
|The Annotated Turing: A Guided Tour Through Alan Turing's Historic Paper on Computability and the Turing Machine
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Author: Charles Petzold
Brand: Petzold Charles
Programming Legend Charles Petzold unlocks the secrets of the extraordinary and prescient 1936 paper by Alan M. Turing
Mathematician Alan Turing invented an imaginary computer known as the Turing Machine; in an age before computers, he explored the concept of what it meant to be computable, creating the field of computability theory in the process, a foundation of present-day computer programming.
The book expands Turing’s original 36-page paper with additional background chapters and extensive annotations; the author elaborates on and clarifies many of Turing’s statements, making the original difficult-to-read document accessible to present day programmers, computer science majors, math geeks, and others.
Interwoven into the narrative are the highlights of Turing’s own life: his years at Cambridge and Princeton, his secret work in cryptanalysis during World War II, his involvement in seminal computer projects, his speculations about artificial intelligence, his arrest and prosecution for the crime of "gross indecency," and his early death by apparent suicide at the age of 41.
- The Annotated Turing A Guided Tour Through Alan Turing s Historic Paper on Computability and the Turing Machine
|Python Machine Learning, 1st Edition
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Author: Sebastian Raschka
Brand: Raschka Sebastian
Unlock deeper insights into Machine Leaning with this vital guide to cutting-edge predictive analytics
About This Book
- Leverage Python's most powerful open-source libraries for deep learning, data wrangling, 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 For
If 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 Pylearn 2 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
Machine 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 Pylearn2, 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 approach
Python 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.
|Interaction Design: Beyond Human-Computer Interaction
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Author: Jenny Preece
A new edition of the #1 text in the Human Computer Interaction field!
Hugely popular with students and professionals alike, Interaction Design is an ideal resource for learning the interdisciplinary skills needed for interaction design, human–computer interaction, information design, web design and ubiquitous computing.
This text offers a cross-disciplinary, practical and process-oriented introduction to the field, showing not just what principles ought to apply to interaction design, but crucially how they can be applied.
An accompanying website contains extensive additional teaching and learning material including slides for each chapter, comments on chapter activities and a number of in-depth case studies written by researchers and designers.
- The Design of Everyday Things: Revised and Expanded Edition
- Systems Analysis and Design
- Introduction to Networks v6 Companion Guide
- Measuring the User Experience, Second Edition: Collecting, Analyzing, and Presenting Usability Metrics (Interactive Technologies)
- The Pocket Universal Principles of Design: 150 Essential Tools for Architects, Artists, Designers, Developers, Engineers, Inventors, and Makers
- The Design of Everyday Things
- Handbook of Usability Testing: How to Plan, Design, and Conduct Effective Tests
- Rocket Surgery Made Easy: The Do-It-Yourself Guide to Finding and Fixing Usability Problems
- Assembly Language for x86 Processors (7th Edition)
- Understanding Your Users, Second Edition: A Practical Guide to User Research Methods (Interactive Technologies)
|Introduction to the Theory of Computation
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Author: Michael Sipser
Brand: Brand: Cengage Learning
Gain a clear understanding of even the most complex, highly theoretical computational theory topics in the approachable presentation found only in the market-leading INTRODUCTION TO THE THEORY OF COMPUTATION, 3E. The number one choice for today's computational theory course, this revision continues the book's well-know, approachable style with timely revisions, additional practice, and more memorable examples in key areas. A new first-of-its-kind theoretical treatment of deterministic context-free languages is ideal for a better understanding of parsing and LR(k) grammars. You gain a solid understanding of the fundamental mathematical properties of computer hardware, software, and applications with a blend of practical and philosophical coverage and mathematical treatments, including advanced theorems and proofs. INTRODUCTION TO THE THEORY OF COMPUTATION, 3E's comprehensive coverage makes this a valuable reference for your continued studies in theoretical computing.
- Used Book in Good Condition
|Text Mining with R: A Tidy Approach
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Author: Julia Silge
Much of the data available today is unstructured and text-heavy, making it challenging for analysts to apply their usual data wrangling and visualization tools. With this practical book, you’ll explore text-mining techniques with tidytext, a package that authors Julia Silge and David Robinson developed using the tidy principles behind R packages like ggraph and dplyr. You’ll learn how tidytext and other tidy tools in R can make text analysis easier and more effective.
The authors demonstrate how treating text as data frames enables you to manipulate, summarize, and visualize characteristics of text. You’ll also learn how to integrate natural language processing (NLP) into effective workflows. Practical code examples and data explorations will help you generate real insights from literature, news, and social media.
- Learn how to apply the tidy text format to NLP
- Use sentiment analysis to mine the emotional content of text
- Identify a document’s most important terms with frequency measurements
- Explore relationships and connections between words with the ggraph and widyr packages
- Convert back and forth between R’s tidy and non-tidy text formats
- Use topic modeling to classify document collections into natural groups
- Examine case studies that compare Twitter archives, dig into NASA metadata, and analyze thousands of Usenet messages
|Machine Learning for Absolute Beginners: A Plain English Introduction
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Author: Oliver Theobald
Ready to crank up a virtual server to smash through petabytes of data? Want to add 'Machine Learning' to your LinkedIn profile?
Well, hold on there...
Before you embark on your epic journey into the world of machine learning, there is basic theory to march through first.
But rather than spend $30-$50 USD on a dense long textbook, you may want to read this book first. As a clear and concise alternative to a textbook, this short book has become a Best Seller on Amazon as a practical and high-level introduction to machine learning.
Machine Learning for Absolute Beginners has been written and designed for absolute beginners. This means plain-English explanations and no coding experience required. Where core algorithms are introduced, clear explanations and visual examples are added to make it easy and engaging to follow along at home.
This title opens with a general introduction to machine learning from a macro level. The second half of the book is more practical and dives into introducing specific algorithms applied in machine learning, including their pros and cons. At the end of the book, I share insights and advice on further learning and careers in this space.
Disclaimer: If you have passed the 'beginner' stage in your study of machine learning and are ready to tackle deep learning and Scikit-learn, you would be well served with a long-format textbook. If, however, you are yet to reach that Lion King moment - as a fully grown Simba looking over the Pride Lands of Africa - then this is the book to gently hoist you up and offer you a clear lay of the land.
In this step-by-step guide you will learn: - The very basics of Machine Learning that all beginners need to master
- Association Analysis used in the retail and E-commerce space
- Recommender Systems as you've seen online, including Amazon
- Decision Trees for visually mapping and classifying decision processes
- Regression Analysis to create trend lines and predict trends
- Data Reduction and Principle Component Analysis to cut through the noise
- k-means and k-nearest Neighbor (k-nn) Clustering to discover new data groupings
- Introduction to Deep Learning/Neural Networks
- Bias/Variance to optimize your machine learning model
- How to build your first machine learning model to predict house values.
- Careers in the field
|The Future of the Professions: How Technology Will Transform the Work of Human Experts
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Author: Richard Susskind
This book predicts the decline of today's professions and describes the people and systems that will replace them. In an Internet society, according to Richard Susskind and Daniel Susskind, we will neither need nor want doctors, teachers, accountants, architects, the clergy, consultants, lawyers, and many others, to work as they did in the 20th century.
The Future of the Professions explains how 'increasingly capable systems' -- from telepresence to artificial intelligence -- will bring fundamental change in the way that the 'practical expertise' of specialists is made available in society.
The authors challenge the 'grand bargain' -- the arrangement that grants various monopolies to today's professionals. They argue that our current professions are antiquated, opaque and no longer affordable, and that the expertise of their best is enjoyed only by a few. In their place, they propose six new models for producing and distributing expertise in society.
The book raises important practical and moral questions. In an era when machines can out-perform human beings at most tasks, what are the prospects for employment, who should own and control online expertise, and what tasks should be reserved exclusively for people?
Based on the authors' in-depth research of more than ten professions, and illustrated by numerous examples from each, this is the first book to assess and question the relevance of the professions in the 21st century.
|The Data Science Design Manual (Texts in Computer Science)
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Author: Steven S. Skiena
This engaging and clearly written textbook/reference provides a must-have introduction to the rapidly emerging interdisciplinary field of data science. It focuses on the principles fundamental to becoming a good data scientist and the key skills needed to build systems for collecting, analyzing, and interpreting data.
The Data Science Design Manual is a source of practical insights that highlights what really matters in analyzing data, and provides an intuitive understanding of how these core concepts can be used. The book does not emphasize any particular programming language or suite of data-analysis tools, focusing instead on high-level discussion of important design principles. This easy-to-read text ideally serves the needs of undergraduate and early graduate students embarking on an “Introduction to Data Science” course. It reveals how this discipline sits at the intersection of statistics, computer science, and machine learning, with a distinct heft and character of its own. Practitioners in these and related fields will find this book perfect for self-study as well.
Additional learning tools:
- Contains “War Stories,” offering perspectives on how data science applies in the real world
- Includes “Homework Problems,” providing a wide range of exercises and projects for self-study
- Provides a complete set of lecture slides and online video lectures at www.data-manual.com
- Provides “Take-Home Lessons,” emphasizing the big-picture concepts to learn from each chapter
- Recommends exciting “Kaggle Challenges” from the online platform Kaggle
- Highlights “False Starts,” revealing the subtle reasons why certain approaches fail
- Offers examples taken from the data science television show “The Quant Shop” (www.quant-shop.com)
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