A classic text by the author who developed ELIZA, a natural-language processing system.

    By presenting actual experiences and analyzing them as they are described, the author conveys the developmental thought processes employed and shows a style of thinking that leads to successful results is something that can be learned. Along with spectacular successes, the author also conveys how failures contributed to shaping the thought processes. Provides the reader with a style of thinking that will enhance a person's ability to function as a problem-solver of complex technical issues. Consists of a collection of stories about the author's participation in significant discoveries, relating how those discoveries came about and, most importantly, provides analysis about the thought processes and reasoning that took place as the author and his associates progressed through engineering problems.

    Each chapter is relatively self-contained and can be used as a unit of study. The algorithms are described in English and in a pseudocode designed to be readable by anyone who has done a little programming. The explanations have been kept elementary without sacrificing depth of coverage or mathematical rigor.

    But what if you could have your computer do them for you?In "Automate the Boring Stuff with Python," you'll learn how to use Python to write programs that do in minutes what would take you hours to do by hand no prior programming experience required. Once you've mastered the basics of programming, you'll create Python programs that effortlessly perform useful and impressive feats of automation to: Search for text in a file or across multiple filesCreate, update, move, and rename files and foldersSearch the Web and download online contentUpdate and format data in Excel spreadsheets of any sizeSplit, merge, watermark, and encrypt PDFsSend reminder emails and text notificationsFill out online formsStep-by-step instructions walk you through each program, and practice projects at the end of each chapter challenge you to improve those programs and use your newfound skills to automate similar tasks.Don't spend your time doing work a well-trained monkey could do. Even if you've never written a line of code, you can make your computer do the grunt work. Learn how in "Automate the Boring Stuff with Python.""

    With this long-awaited revision, the authors continue to present the theory in a concise and straightforward manner, now with an eye out for the practical applications. They have revised this book to make it more accessible to today's students, including the addition of more material on writing proofs, more figures and pictures to convey ideas, side-boxes to highlight other interesting material, and a less formal writing style. Exercises at the end of each chapter, including some new, easier exercises, help readers confirm and enhance their understanding of the material. *NEW! Completely rewritten to be less formal, providing more accessibility to todays students. *NEW! Increased usage of figures and pictures to help convey ideas. *NEW! More detail and intuition provided for definitions and proofs. *NEW! Provides special side-boxes to present supplemental material that may be of interest to readers. *NEW! Includes more exercises, including many at a lower level. *NEW! Presents program-like notation for PDAs and Turing machines. *NEW! Increas

    I. is explored and explained in this best selling text. Assuming no prior knowledge, it covers topics like neural networks and robotics. This text explores the range of problems which have been and remain to be solved using A. I. tools and techniques. The second half of this text is an excellent reference.

    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

    Umadathan.Popularly known as the 'Sherlock Holmes of Kerala', Dr Umadathan revisits some of his strangest and most interesting cases, like the Chacko murder masterminded by Sukumara Kurup; the sensational Polakkulam case; and the baffling Panoor Soman case.Chilling, shocking and, at times, downright bizarre, Dead Men Tell Tales is unputdownable.

     If you have ever found yourself lost halfway through other introductory materials on this topic, this is the book for you. If you don't understand set terminology such as vectors, hyperplanes, and centroids, then this is also the book for you. This starter course isn't a picture story book but does include many visual examples that break algorithms down into a digestible and practical format. As a starter course, this book connects the dots and offers the crash course I wish I had when I first started. The kind of guide I wish had before I started taking on introductory courses that presume you’re two days away from an advanced mathematics exam. That’s why this introductory course doesn’t go further on the subject than other introductory books, but rather, goes a step back. A half-step back in order to help everyone make his or her first strides in machine learning and is an ideal study companion for the visual learner. In this step-by-step guide you will learn: - How to download free datasets - What tools and software packages you need - Data scrubbing techniques, including one-hot encoding, binning and dealing with missing data - Preparing data for analysis, including k-fold Validation - Regression analysis to create trend lines - Clustering, including k-means and k-nearest Neighbors - Naive Bayes Classifier to predict new classes - Anomaly detection and SVM algorithms to combat anomalies and outliers - The basics of Neural Networks - Bias/Variance to improve your machine learning model - Decision Trees to decode classification Please feel welcome to join this starter course by buying a copy, or sending a free sample to your preferred device.

    Named one of the greatest minds of the 20th century by Time, Tim Berners-Lee is responsible for one of that century's most important advancements: the world wide web.  Now, this low-profile genius - who never personally profited from his invention - offers a compelling portrait of his invention.  He reveals the Web's origins and the creation of the now ubiquitous http and www acronyms and shares his views on such critical issues as censorship, privacy, the increasing power of software companies, and the need to find the ideal balance between commercial and social forces.  He offers insights into the true nature of the Web, showing readers how to use it to its fullest advantage.  And he presents his own plan for the Web's future, calling for the active support and participation of programmers, computer manufacturers, and social organizations to manage and maintain this valuable resource so that it can remain a powerful force for social change and an outlet for individual creativity.

    These days, linger over a Web page selling lamps, and they will turn up at the advertising margins as you move around the Internet, reminding you, tempting you to make that purchase. Search engines such as Google can now look deep into the data on the Web to pull out instances of the words you are looking for. And there are pages that collect and assess information to give you a snapshot of changing political opinion. These are just basic examples of the growth of Web intelligence, as increasingly sophisticated algorithms operate on the vast and growing amount of data on the Web, sifting, selecting, comparing, aggregating, correcting; following simple but powerful rules to decide what matters. While original optimism for Artificial Intelligence declined, this new kind of machine intelligence is emerging as the Web grows ever larger and more interconnected.Gautam Shroff takes us on a journey through the computer science of search, natural language, text mining, machine learning, swarm computing, and semantic reasoning, from Watson to self-driving cars. This machine intelligence may even mimic at a basic level what happens in the brain.

    How can today's computers perform such a bewildering variety of tasks if computing is just glorified arithmetic? The answer, as Martin Davis lucidly illustrates, lies in the fact that computers are essentially engines of logic. Their hardware and software embody concepts developed over centuries by logicians such as Leibniz, Boole, and Godel, culminating in the amazing insights of Alan Turing. The Universal Computer traces the development of these concepts by exploring with captivating detail the lives and work of the geniuses who first formulated them. Readers will come away with a revelatory understanding of how and why computers work and how the algorithms within them came to be.

    The power of an artificial intelligence (AI) comes from its intelligence, not physical strength and laser guns. Humans steer the future not because we're the strongest or the fastest but because we're the smartest. When machines become smarter than humans, we'll be handing them the steering wheel. What promises—and perils—will these powerful machines present? Stuart Armstrong’s new book navigates these questions with clarity and wit.Can we instruct AIs to steer the future as we desire? What goals should we program into them? It turns out this question is difficult to answer! Philosophers have tried for thousands of years to define an ideal world, but there remains no consensus. The prospect of goal-driven, smarter-than-human AI gives moral philosophy a new urgency. The future could be filled with joy, art, compassion, and beings living worthwhile and wonderful lives—but only if we’re able to precisely define what a "good" world is, and skilled enough to describe it perfectly to a computer program.AIs, like computers, will do what we say—which is not necessarily what we mean. Such precision requires encoding the entire system of human values for an AI: explaining them to a mind that is alien to us, defining every ambiguous term, clarifying every edge case. Moreover, our values are fragile: in some cases, if we mis-define a single piece of the puzzle—say, consciousness—we end up with roughly 0% of the value we intended to reap, instead of 99% of the value.Though an understanding of the problem is only beginning to spread, researchers from fields ranging from philosophy to computer science to economics are working together to conceive and test solutions. Are we up to the challenge?A mathematician by training, Armstrong is a Research Fellow at the Future of Humanity Institute (FHI) at Oxford University. His research focuses on formal decision theory, the risks and possibilities of AI, the long term potential for intelligent life (and the difficulties of predicting this), and anthropic (self-locating) probability. Armstrong wrote Smarter Than Us at the request of the Machine Intelligence Research Institute, a non-profit organization studying the theoretical underpinnings of artificial superintelligence.

    The gold standard for attaining security is cryptography because it provides the most reliable tools for storing or transmitting digital information. Written by Niels Ferguson, lead cryptographer for Counterpane, Bruce Schneier's security company, and Bruce Schneier himself, this is the much anticipated follow-up book to Schneier's seminal encyclopedic reference, Applied Cryptography, Second Edition (0-471-11709-9), which has sold more than 150,000 copies. Niels Ferguson (Amsterdam, Netherlands) is a cryptographic engineer and consultant at Counterpane Internet Security. He has extensive experience in the creation and design of security algorithms, protocols, and multinational security infrastructures. Previously, Ferguson was a cryptographer for DigiCash and CWI. At CWI he developed the first generation of off-line payment protocols. He has published numerous scientific papers. Bruce Schneier (Minneapolis, MN) is Founder and Chief Technical Officer at Counterpane Internet Security, a managed-security monitoring company. He is also the author of Secrets and Lies: Digital Security in a Networked World (0-471-25311-1).

    This guide introduces new features and capabilities, providing a practical, high-level overview for IT professionals ready to begin deployment planning now. This book is a preview, a work in progress about a work in progress. It offers a snapshot of the Windows 10 Technical Preview as of April 2015, on the eve of the BUILD Developers’ Conference in San Francisco.