Conflict between humans and machines is seen as inevitable and its outcome all too predictable.In this groundbreaking book, distinguished AI researcher Stuart Russell argues that this scenario can be avoided, but only if we rethink AI from the ground up. Russell begins by exploring the idea of intelligence in humans and in machines. He describes the near-term benefits we can expect, from intelligent personal assistants to vastly accelerated scientific research, and outlines the AI breakthroughs that still have to happen before we reach superhuman AI. He also spells out the ways humans are already finding to misuse AI, from lethal autonomous weapons to viral sabotage.If the predicted breakthroughs occur and superhuman AI emerges, we will have created entities far more powerful than ourselves. How can we ensure they never, ever, have power over us? Russell suggests that we can rebuild AI on a new foundation, according to which machines are designed to be inherently uncertain about the human preferences they are required to satisfy. Such machines would be humble, altruistic, and committed to pursue our objectives, not theirs. This new foundation would allow us to create machines that are provably deferential and provably beneficial.In a 2014 editorial co-authored with Stephen Hawking, Russell wrote, "Success in creating AI would be the biggest event in human history. Unfortunately, it might also be the last." Solving the problem of control over AI is not just possible; it is the key that unlocks a future of unlimited promise.

    We are living in an age where data comes in abundance, and thanks to the self-learning algorithms from the field of machine learning, we can turn this data into knowledge. Automated speech recognition on our smart phones, web search engines, e-mail spam filters, the recommendation systems of our favorite movie streaming services – machine learning makes it all possible.Thanks to the many powerful open-source libraries that have been developed in recent years, machine learning is now right at our fingertips. Python provides the perfect environment to build machine learning systems productively.This book will teach you the fundamentals of machine learning and how to utilize these in real-world applications using Python. Step-by-step, you will expand your skill set with the best practices for transforming raw data into useful information, developing learning algorithms efficiently, and evaluating results.You will discover the different problem categories that machine learning can solve and explore how to classify objects, predict continuous outcomes with regression analysis, and find hidden structures in data via clustering. You will build your own machine learning system for sentiment analysis and finally, learn how to embed your model into a web app to share with the world

    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.

    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.

    This guide also helps you understand the many data-mining techniques in use today.Based on an MBA course Provost has taught at New York University over the past ten years, Data Science for Business provides examples of real-world business problems to illustrate these principles. You’ll not only learn how to improve communication between business stakeholders and data scientists, but also how participate intelligently in your company’s data science projects. You’ll also discover how to think data-analytically, and fully appreciate how data science methods can support business decision-making.Understand how data science fits in your organization—and how you can use it for competitive advantageTreat data as a business asset that requires careful investment if you’re to gain real valueApproach business problems data-analytically, using the data-mining process to gather good data in the most appropriate wayLearn general concepts for actually extracting knowledge from dataApply data science principles when interviewing data science job candidates

    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

    He looks like us, he lives like us... but he is not one of us. Kalian knows nothing outside of his mundane life teaching history on 30th century Earth, until a day like any other triggers a series of events, which will tie his fate to that of humanity. A human hand print, embedded into a rock with alien script, is discovered on a moon that mankind has never set foot on. This discovery holds a secret, which will sweep Kalian into the heart of a conspiracy that has corrupted the galaxy for two hundred thousand years.

    But the ghosts of Madison's war in Afghanistan have wreaked havoc on his life and put his marriage on the skids. Kicked out of home by the woman he loves, Madison wants back in but has much to prove and a lot of trust to regain. His big chance arrives in the form of a case that looks hopeless at first, but Madison never loses faith. In this courtroom drama, the personal and professional become tightly intertwined—Madison fights with all he's got because it's all on the line.

    As he begins to discover the psychic abilities within him, he comes in contact with Dr. Myra, who helps him through a journey of self-discovery. Past life regression therapy helps him understand the glorious lives he has led in his previous births. Will this help Vikram understand the purpose of his current life? Can divine guidance help him cope with the effects of his past and help him realize his full potential? Will he get past the challenges on the way and fulfill his life purpose? Read The Awakening of an Indigo to find out.

    An Arizona lawman who rides rough country, often going up against dangerous men and gangs alone. Dealing with bank robbers, kidnappers and rustlers with his fast gun. Much of his tracking ability comes from his Scottish father, who served as an Indian scout. Valuable experience as a Rough Rider with Teddy Roosevelt, then as an Arizona Ranger.Outlaws and corrupt government tend to stand in Duncan’s way, but he manages to overcome all obstacles with integrity and really fast guns.

     Daniel Silva is one of the world's most popular writers of thriller and espionage fiction, with numerous novels in various series and collections. As a fan, it can be difficult to keep up! The Reader's Friend reference list for Daniel Silva is a complete list of every Daniel Silva title, and is designed for maximum convenience and functionality. Keeping track of your Silva addiction had never been easier! All of the info you need, and none of the clutter you don't. We're readers just like you, and we know you don't need a lot of junk in your reference list. Our Daniel Silva list is designed to be clean and optimally usable: The info you need, right there where you need it. No flipping back and forth in the table of contents, no scrolling through filler. AND IT'S SO SIMPLE TO USE: Check off the books you've read with a single tap! — Just use your Kindle's built-in highlighter. Instructions are included! Get free updates regardless of how you purchased the list — We update immediately every time a new Daniel Silva title is released, so you'll never be out of the loop! Browse titles BY RELEASE DATE, BY SERIES, or BY OMNIBUS EDITION — Whichever is most convenient for you! For a preview, just click the book cover to the left and "LOOK INSIDE!" EVERY DANIEL SILVA TITLE IS INCLUDED: All titles listed in order of release date Michael Osbourne series in reading order Gabriel Allon series in reading order Stand-Alone novels All Gabriel Allon omnibus editions PLEASE NOTE: THIS IS A TITLE LIST ONLY, compiled for reference purposes to assist readers. No copyrighted material from the titles listed is included. This list is compliant with United States Copyright Office circular 34.

    She has taken it upon herself to help Professor Balder whose trade secrets have been stolen. She finds the leak and then has to step back as the good professor deals with his own problems. When the professor is brutally murdered in front of his son on a night when he was going to spill all to a journalist, Lisbeth feels she can take matters into her own hands. Through a bit of hacking genius and the help of Balder’s autistic son, Lisbeth exposes high ranking government officials and her father’s own criminal connections from years past. Though she knows her evil twin Camilla is still after her, she fights the good fight, until next time. PLEASE NOTE: This is a Summary and Analysis of the book and NOT the original book. This companion includes the following: ► Book Review ► Character List ► Summary of the Chapters ► Discussion Questions ► Analysis of Themes & Symbols This Analysis fills the gap, making you understand more while enhancing your reading experience. SPECIAL OFFER $2.99 (Regularly priced: $4.99) About the Author: Leopard Books, is your perfect quick read companion. We analyze every chapter and hunt down the key points for your convenience. With in-depth summary and analysis, leap through books quickly and with ease.

    Introducing students to the many facets of neural networks, this text provides many case studies to illustrate their real-life, practical applications.

    Machine learning provides these, developing methods that can automatically detect patterns in data and then use the uncovered patterns to predict future data. 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 necessary 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. The book is suitable for upper-level undergraduates with an introductory-level college math background and beginning graduate students.

    Harrowing and heroic, the events of D-Day were recorded in the personal writings of those who were there. Here, Francis Crosby has compiled a comprehensive collection of previously unpublished letters, diaries, photographs, and reminiscences that tell the story of D-Day as it has never been told before.With the use of new international archives, Crosby has culled vivid and detailed eye-witness accounts from each beach, as well as perspectives from land, sea, and air. This fascinating collection includes entries from American, British, and Canadian troops, the Merchant Navy and the Royal Air Force, and newly available German materials. Also included are contemporary and retrospective reactions of women "in the know" and those whom knew from "unofficial sources" of the immediate imminence of the assault.