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.

    A class can be skilled and motivated to learn without a teacher always having to lead. Engaging learners in this way unpicks intrinsic motivation, the foundation that underpins a productive learning environment and helps to develop independent learning.Based on five years of intensive research through Osiris Education's award-winning Outstanding Teaching Intervention program this book is packed with proven advice and innovative tools that were developed in these successful outstanding lessons. Written in the same humorous, thought-provoking style with which they both teach and train, the authors aim to challenge all who teach, from newly qualified teachers to seasoned professionals, to reflect on their day-to-day practice and set an agenda for sustainable improvement.

    If we don't candidly answer the pertinent questions, we will only paint a false picture.We are standing at a crucial and pivotal point in history. It's time for diversity in AI. This unprecedented technology will affect society as a whole and we need individuals from diverse disciplines and backgrounds to join the discussion. The issues surrounding AI can't be left to a small group of scientists, technologists or business executives to address. Our future and our children's future are at stake.More than ever, we need leaders who will stand on integrity and who will put people first.Do you want to take a glimpse into the future of leadership? The Future of Leadership: Rise of Automation, Robotics and Artificial Intelligence offers the most comprehensive view of what is taking place in the world of AI and emerging technologies, and gives valuable insights that will allow you to successfully navigate the tsunami of technology that is coming our way.

    With the sophisticated algorithms in this book, you can write smart programs to access interesting datasets from other web sites, collect data from users of your own applications, and analyze and understand the data once you've found it.Programming Collective Intelligence takes you into the world of machine learning and statistics, and explains how to draw conclusions about user experience, marketing, personal tastes, and human behavior in general -- all from information that you and others collect every day. Each algorithm is described clearly and concisely with code that can immediately be used on your web site, blog, Wiki, or specialized application. This book explains:Collaborative filtering techniques that enable online retailers to recommend products or media Methods of clustering to detect groups of similar items in a large dataset Search engine features -- crawlers, indexers, query engines, and the PageRank algorithm Optimization algorithms that search millions of possible solutions to a problem and choose the best one Bayesian filtering, used in spam filters for classifying documents based on word types and other features Using decision trees not only to make predictions, but to model the way decisions are made Predicting numerical values rather than classifications to build price models Support vector machines to match people in online dating sites Non-negative matrix factorization to find the independent features in a dataset Evolving intelligence for problem solving -- how a computer develops its skill by improving its own code the more it plays a game Each chapter includes exercises for extending the algorithms to make them more powerful. Go beyond simple database-backed applications and put the wealth of Internet data to work for you. "Bravo! I cannot think of a better way for a developer to first learn these algorithms and methods, nor can I think of a better way for me (an old AI dog) to reinvigorate my knowledge of the details."-- Dan Russell, Google "Toby's book does a great job of breaking down the complex subject matter of machine-learning algorithms into practical, easy-to-understand examples that can be directly applied to analysis of social interaction across the Web today. If I had this book two years ago, it would have saved precious time going down some fruitless paths."-- Tim Wolters, CTO, Collective Intellect

    Chain mail, also known as chainmail, maille, or chainmaille is a great hobby for any age that doesn't involve just making armor.You can make almost anything from chainmail. Below are just a few examples of what you can make:• Jewerly (Bracelets, Necklaces, Anklets, Earrings, Rings)• Clothing (Bikini tops, Belts, Shirts, Ties)• Art• Armor• Inlays (pictures in your maille)• And much, much more!In this beginner's book you'll learn the following:• A short history of chain mail• Different rings used and the kinds of metals• How to open and close rings correctly• Tools used like pliers and different kinds• An overview of the family of chainmail weaves• How to weave 7 of the more common, simpler weaves shown in 3D graphics• Resources for purchasing rings and tools3 Starter Projects:• Women's Choker Necklace• Men's Flat Bracelet• Pouch or Dice Bag Chainmail is neither difficult to learn nor expensive. All it takes is a willingness to try something new no matter your age, education, or income! Scoll up and click the "Look Inside" feature on the top left hand side of this page to see what's included in this book.A Personal Note From The Author:This beginner's guide blends 10 years of my personal experience chainmailling. When I first started chainmailling, I knew nothing about it except what I'd seen online or in movies; chainmail armor. Not very exciting for the average person but the idea of weaving metal rings into something I could create without any special skills appealed to me. I stumbled across The Ring Lord, a site that sells all kinds of rings in all kinds of sizes, colors, and metals, and I was hooked!Now ten years later after creating all sorts of projects from maille, some things I wouldn't have thought possible, I wanted to share what I've learned with someone just starting out. This first volume is not meant to cover everything about chainmailling nor does it include dozens of projects. It's meant for someone who's never mailled before or who's only dabbled a little but now wants a simple-to-follow guide with clear instructions and pictures on how to get started mailling immediately with the most common weaves applied to a couple starter projects.

    But as anyone who has ever opened up a spreadsheet packed with seemingly infinite lines of data knows, numbers aren't enough: we need to know how to make those numbers talk. In The Model Thinker, social scientist Scott E. Page shows us the mathematical, statistical, and computational models—from linear regression to random walks and far beyond—that can turn anyone into a genius. At the core of the book is Page's "many-model paradigm," which shows the reader how to apply multiple models to organize the data, leading to wiser choices, more accurate predictions, and more robust designs. The Model Thinker provides a toolkit for business people, students, scientists, pollsters, and bloggers to make them better, clearer thinkers, able to leverage data and information to their advantage.

    A guide to the Nikon D3100 camera provides information on the camera's scene modes, composition, focus, lighting, and composition to take successful portraits and sports and landscape photographs.

    With "Web Design For Dummies," you will be able to design your own Web site like a pro.Web design requires many programs to make a Website attractive and fun, including: Using Web editors like DreamweaverImage editing tools like Photoshop elementsDrawing utensils like IllustratorBackground markup and scripting languages like HTML and CSSThis fun guide covers all of the topics that every aspiring Web designer should know. This book offers advice on: Designing for your audienceBuilding a solid framework for easy navigationCreating appealing graphics that work with the siteChoosing the proper type and colorsTweaking the HTML to make everything work correctlyApplying next-step technologies including JavaScriptParlaying your skills into paid workWith expert guidance from Lisa Lopuck, a pioneer in interactive media design and the Senior Producer at Disney, you will be creating superb Web pages that will charm and impress all of your visitors

    With it has come vast amounts of data in a variety of fields such as medicine, biology, finance, and marketing. The challenge of understanding these data has led to the development of new tools in the field of statistics, and spawned new areas such as data mining, machine learning, and bioinformatics. Many of these tools have common underpinnings but are often expressed with different terminology. This book describes the important ideas in these areas in a common conceptual framework. While the approach is statistical, the emphasis is on concepts rather than mathematics. Many examples are given, with a liberal use of color graphics. It should be a valuable resource for statisticians and anyone interested in data mining in science or industry. The book's coverage is broad, from supervised learning (prediction) to unsupervised learning. The many topics include neural networks, support vector machines, classification trees and boosting—the first comprehensive treatment of this topic in any book. Trevor Hastie, Robert Tibshirani, and Jerome Friedman are professors of statistics at Stanford University. They are prominent researchers in this area: Hastie and Tibshirani developed generalized additive models and wrote a popular book of that title. Hastie wrote much of the statistical modeling software in S-PLUS and invented principal curves and surfaces. Tibshirani proposed the Lasso and is co-author of the very successful An Introduction to the Bootstrap. Friedman is the co-inventor of many data-mining tools including CART, MARS, and projection pursuit.

    In this book, you’ll learn how many of the most fundamental data science tools and algorithms work by implementing them from scratch. If you have an aptitude for mathematics and some programming skills, author Joel Grus will help you get comfortable with the math and statistics at the core of data science, and with hacking skills you need to get started as a data scientist. Today’s messy glut of data holds answers to questions no one’s even thought to ask. This book provides you with the know-how to dig those answers out. Get a crash course in Python Learn the basics of linear algebra, statistics, and probability—and understand how and when they're used in data science Collect, explore, clean, munge, and manipulate data Dive into the fundamentals of machine learning Implement models such as k-nearest Neighbors, Naive Bayes, linear and logistic regression, decision trees, neural networks, and clustering Explore recommender systems, natural language processing, network analysis, MapReduce, and databases

    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.

    This concise introduction shows you how to perform statistical analysis computationally, rather than mathematically, with programs written in Python.You'll work with a case study throughout the book to help you learn the entire data analysis process—from collecting data and generating statistics to identifying patterns and testing hypotheses. Along the way, you'll become familiar with distributions, the rules of probability, visualization, and many other tools and concepts.Develop your understanding of probability and statistics by writing and testing codeRun experiments to test statistical behavior, such as generating samples from several distributionsUse simulations to understand concepts that are hard to grasp mathematicallyLearn topics not usually covered in an introductory course, such as Bayesian estimationImport data from almost any source using Python, rather than be limited to data that has been cleaned and formatted for statistics toolsUse statistical inference to answer questions about real-world data

    However, most discussions of Bayesian inference rely on intensely complex mathematical analyses and artificial examples, making it inaccessible to anyone without a strong mathematical background. Now, though, Cameron Davidson-Pilon introduces Bayesian inference from a computational perspective, bridging theory to practice-freeing you to get results using computing power. Bayesian Methods for Hackers illuminates Bayesian inference through probabilistic programming with the powerful PyMC language and the closely related Python tools NumPy, SciPy, and Matplotlib. Using this approach, you can reach effective solutions in small increments, without extensive mathematical intervention. Davidson-Pilon begins by introducing the concepts underlying Bayesian inference, comparing it with other techniques and guiding you through building and training your first Bayesian model. Next, he introduces PyMC through a series of detailed examples and intuitive explanations that have been refined after extensive user feedback. You'll learn how to use the Markov Chain Monte Carlo algorithm, choose appropriate sample sizes and priors, work with loss functions, and apply Bayesian inference in domains ranging from finance to marketing. Once you've mastered these techniques, you'll constantly turn to this guide for the working PyMC code you need to jumpstart future projects. Coverage includes - Learning the Bayesian "state of mind" and its practical implications - Understanding how computers perform Bayesian inference - Using the PyMC Python library to program Bayesian analyses - Building and debugging models with PyMC - Testing your model's "goodness of fit" - Opening the "black box" of the Markov Chain Monte Carlo algorithm to see how and why it works - Leveraging the power of the "Law of Large Numbers" - Mastering key concepts, such as clustering, convergence, autocorrelation, and thinning - Using loss functions to measure an estimate's weaknesses based on your goals and desired outcomes - Selecting appropriate priors and understanding how their influence changes with dataset size - Overcoming the "exploration versus exploitation" dilemma: deciding when "pretty good" is good enough - Using Bayesian inference to improve A/B testing - Solving data science problems when only small amounts of data are available Cameron Davidson-Pilon has worked in many areas of applied mathematics, from the evolutionary dynamics of genes and diseases to stochastic modeling of financial prices. His contributions to the open source community include lifelines, an implementation of survival analysis in Python. Educated at the University of Waterloo and at the Independent University of Moscow, he currently works with the online commerce leader Shopify.

    These topics lie at the heart of many exciting areas of contemporary science and engineering - communication, signal processing, data mining, machine learning, pattern recognition, computational neuroscience, bioinformatics, and cryptography. This textbook introduces theory in tandem with applications. Information theory is taught alongside practical communication systems, such as arithmetic coding for data compression and sparse-graph codes for error-correction. A toolbox of inference techniques, including message-passing algorithms, Monte Carlo methods, and variational approximations, are developed alongside applications of these tools to clustering, convolutional codes, independent component analysis, and neural networks. The final part of the book describes the state of the art in error-correcting codes, including low-density parity-check codes, turbo codes, and digital fountain codes -- the twenty-first century standards for satellite communications, disk drives, and data broadcast. Richly illustrated, filled with worked examples and over 400 exercises, some with detailed solutions, David MacKay's groundbreaking book is ideal for self-learning and for undergraduate or graduate courses. Interludes on crosswords, evolution, and sex provide entertainment along the way. In sum, this is a textbook on information, communication, and coding for a new generation of students, and an unparalleled entry point into these subjects for professionals in areas as diverse as computational biology, financial engineering, and machine learning.

    Tricks will help you to understand, learn and remember Indian Polity and Constitution. Mnemonics used in this book are very unique. This book will help to cover other subjects such as Social Science, Political Science, Public Administration, Constitutional Law, Legal Reference and Current Affairs in a better way. This Textbook and eTextbook will be useful for UPSC, PPSC, HPSC, State Competitive Examinations, SSC, Banking, Clerical and all other Government Examinations. In short, this book is a sure-shot formula for success with its tips and tricks. Contents: Title Page Objectives What is the need for a political system in a country? Schedules of Indian Constitution  The Preamble The Union and its Territory: Part I (Articles 1- 4) Citizenship: Part II(Articles 5- 11) Fundamental Rights: Part III (Article 12-35) Directive Principles of State Policy: Part IV (Article 36-51) Fundamental Duties: Part IV-A(Article 51A) Union : Part V (Article 52-151) Other important Articles of the Indian Constitution Extended learning