Nine months later, a call from the adoption agency plunges them into every parent's worst nightmare: the father never signed away his parental rights, and now he wants his daughter back. The teenage biological father has no love for his daughter and Jack and Melissa can't understand why he and his father want the girl so badly. With three weeks until they must legally hand over their baby, just how far outside the law are they prepared to go to find out? Blue Heaven If twelve-year-old Annie hadn't been angry with her mother, she would never have taken her younger brother William on a secret fishing trip deep into the North Idaho woods and they would never have witnessed the execution. Now they're running for their lives. There's nowhere for William and Annie to hide. And no one they can trust. Until they meet Jess Rawlins. Rawlins, an old-school rancher, knows something is wrong with the law in Blue Heaven. But he is only one against four men who will stop at nothing to silence their witnesses... Back of Beyond When Detective Cody Hoyt finds the dead body of his AA sponsor in a burned-out mountain cabin, he doesn't know what to believe. Was this the last desperate act of an addict? Or is that exactly what someone wants him to think? Cody's determined to avenge the man who was always there for him and, when evidence incriminates a wilderness guide, Hoyt is ready to track him down. The only problem is the guide has Cody's son, and he's taking him into the back of beyond.

    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

    What should you and your company be doing today to ensure that you're poised for success and keeping up with your competitors in the age of AI?Artificial Intelligence: The Insights You Need from Harvard Business Review brings you today's most essential thinking on AI and explains how to launch the right initiatives at your company to capitalize on the opportunity of the machine intelligence revolution.Business is changing. Will you adapt or be left behind?Get up to speed and deepen your understanding of the topics that are shaping your company's future with the Insights You Need from Harvard Business Review series. Featuring HBR's smartest thinking on fast-moving issues--blockchain, cybersecurity, AI, and more--each book provides the foundational introduction and practical case studies your organization needs to compete today and collects the best research, interviews, and analysis to get it ready for tomorrow. You can't afford to ignore how these issues will transform the landscape of business and society. The Insights You Need series will help you grasp these critical ideas--and prepare you and your company for the future.

    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.

    But they decide to take a detour via Jeffrey's hometown and things go violently wrong when Jeffrey's best friend Robert shoots dead an intruder who breaks into his house.Jeffrey and Sara are first on the scene and Jeffrey's keen to clear his friend's name, but for Sara things aren't so simple. The sear marks around the bullet-hole don't tally with Robert's story. Robert's wife, Jessie, is incoherent and confused. And when Jeffrey appears to change the crime scene, Sara no longer knows who to trust.Twelve years later, Sara and Jeffrey are caught up in a shockingly brutal attack which threatens to destroy both their lives. But they're not random victims. They've been targeted. And it seems the past is catching up with both of them ...FaithlessThere are many ways to die. But some are more terrifying than others ...A walk in the woods takes a sinister turn for police chief Jeffrey Tolliver and medical examiner Sara Linton when they stumble across the body of a young girl. Incarcerated in the ground, all the initial evidence indicates that she has, quite literally, been scared to death.But as Sara embarks on the autopsy, something even more horrifying comes to light. Something which shocks even Sara. Detective Lena Adams, talented but increasingly troubled, is called in from vacation to help with the investigation - and the trail soon leads to the neighbouring county, an isolated community, and a terrible secret ...Skin PrivilegeLena Adams has spent her life struggling to escape her past. She has only unhappy memories of Reece, the small town which nearly destroyed her. She's made a new life for herself as a police detective in Heartsdale, a hundred miles away - but nothing could prepare her for the violence which explodes when she is forced to return. A vicious murder leaves a young woman incinerated beyond recognition. And Lena is the only suspect.When Heartsdale police chief Jeffrey Tolliver, Lena's boss, receives word that his detective has been arrested, he has no choice but to go to Lena's aid - taking with him his wife, medical examiner Sara Linton. But soon after their arrival, a second victim is found. The town closes ranks. And both Jeffrey and Sara find themselves entangled in a horrifying underground world of bigotry and rage - a violent world which shocks even them. A world which puts their own lives in jeopardy. Only Jeffrey and Sara can free Lena from the web of lies, betrayal and brutality that has trapped her. But can they discover the truth before the killer strikes again?

    The book is a complete revision of the commands section of Sobell's Practical Guide to Linux - a proven best-seller. The book is Linux distribution and release agnostic. It will appeal to users of ALL Linux distributions. Superior examples make this book the the best option on the market! System administrators, software developers, quality assurance engineers and others working on a Linux system need to work from the command line in order to be effective. Linux is famous for its huge number of command line utility programs, and the programs themselves are famous for their large numbers of options, switches, and configuration files. But the truth is that users will only use a limited (but still significant) number of these utilities on a recurring basis, and then only with a subset of the most important and useful options, switches and configuration files. This book cuts through all the noise and shows them which utilities are most useful, and which options most important. And it contains examples, lot's and lot's of examples. programmability. Utilities are designed, by default, to work wtih other utilities within shell programs as a way of automating system tasks. This book contains a superb introduction to Linux shell programming. And since shell programmers need to write their programs in text editors, this book covers the two most popular ones: vi and emacs.

    This report provides a brief yet comprehensive introduction to Python for engineers, researchers, and data scientists who are already familiar with another programming language.Author Jake VanderPlas, an interdisciplinary research director at the University of Washington, explains Python’s essential syntax and semantics, built-in data types and structures, function definitions, control flow statements, and more, using Python 3 syntax.You’ll explore:- Python syntax basics and running Python codeBasic semantics of Python variables, objects, and operators- Built-in simple types and data structures- Control flow statements for executing code blocks conditionally- Methods for creating and using reusable functionsIterators, list comprehensions, and generators- String manipulation and regular expressions- Python’s standard library and third-party modules- Python’s core data science tools- Recommended resources to help you learn more

    The most enigmatic artifact in existence. Drawn by an Ottoman Admiral in 1513 and taken from much more ancient maps, it displays sections of Antarctica that could only have been visible from the air before the great ice age nearly 6000 years ago.Explorer and treasure hunter Dillon Mentzer believes the information in the map was discovered by an advanced civilization now lost to history. When word reaches him that an expedition is being mounted by a man claiming to have discovered a lost city, Dillon takes the chance and joins with him.THE MOST POWERFUL WEAPON OF THE ANCIENT WORLD...What Dillon finds is a civilization more advanced than our own with a terrible secret: a weapon that has remained hidden for sixty centuries. When the weapon, dubbed "Black Onyx," is exposed, Dillon decides it's too powerful to leave and takes it back with him.AN EVIL THAT WILL STOP AT NOTHING...When the Black Onyx is revealed, El Sacerdote, the most powerful man in the Juarez cartel, learns of its power. He will destroy anyone and everything to get it. He kidnaps those closest to Dillon who reveal to him that there is not one, but multiple Black Onyx suits. El Sacerdote travels to the Antarctic to retrieve a weapon that will allow him to butcher any who oppose him.With the fate of the world in his hands, Dillon must choose whether the fight is worth it. But something he could never have imagined awaits him...an entity more powerful than even the Black Onyx.A short novel.ABOUT THE AUTHORVictor Methos is the author of fifteen bestselling novels and his books have topped the bestseller charts year after year. Coming from a mathematics and philosophy background, he is currently on a quest to climb the "Seven Summits," the seven highest mountains on earth.273 KB

    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

    Its techniques are widely applied in engineering, science, finance, and commerce. This book is designed for a short course on machine learning. It is a short course, not a hurried course. From over a decade of teaching this material, we have distilled what we believe to be the core topics that every student of the subject should know. We chose the title `learning from data' that faithfully describes what the subject is about, and made it a point to cover the topics in a story-like fashion. Our hope is that the reader can learn all the fundamentals of the subject by reading the book cover to cover. ---- Learning from data has distinct theoretical and practical tracks. In this book, we balance the theoretical and the practical, the mathematical and the heuristic. Our criterion for inclusion is relevance. Theory that establishes the conceptual framework for learning is included, and so are heuristics that impact the performance of real learning systems. ---- Learning from data is a very dynamic field. Some of the hot techniques and theories at times become just fads, and others gain traction and become part of the field. What we have emphasized in this book are the necessary fundamentals that give any student of learning from data a solid foundation, and enable him or her to venture out and explore further techniques and theories, or perhaps to contribute their own. ---- The authors are professors at California Institute of Technology (Caltech), Rensselaer Polytechnic Institute (RPI), and National Taiwan University (NTU), where this book is the main text for their popular courses on machine learning. The authors also consult extensively with financial and commercial companies on machine learning applications, and have led winning teams in machine learning competitions.

    As an AI researcher with 25 years of experience, professor Mike Wooldridge has learned to be obsessively cautious about such claims, while still promoting an intense optimism about the future of the field. There have been genuine scientific breakthroughs that have made AI systems possible in the past decade that the founders of the field would have hailed as miraculous. Driverless cars and automated translation tools are just two examples of AI technologies that have become a practical, everyday reality in the past few years, and which will have a huge impact on our world.While the dream of conscious machines remains, Professor Wooldridge believes, a distant prospect, the floodgates for AI have opened. Wooldridge's A Brief History of Artificial Intelligence is an exciting romp through the history of this groundbreaking field--a one-stop-shop for AI's past, present, and world-changing future.

    But building a machine learning system requires that you make practical decisions: Should you collect more training data? Should you use end-to-end deep learning? How do you deal with your training set not matching your test set? and many more. Historically, the only way to learn how to make these "strategy" decisions has been a multi-year apprenticeship in a graduate program or company. This is a book to help you quickly gain this skill, so that you can become better at building AI systems.

    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!

    This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree- based methods, support vector machines, clustering, and more. Color graphics and real-world examples are used to illustrate the methods presented. Since the goal of this textbook is to facilitate the use of these statistical learning techniques by practitioners in science, industry, and other fields, each chapter contains a tutorial on implementing the analyses and methods presented in R, an extremely popular open source statistical software platform. Two of the authors co-wrote The Elements of Statistical Learning (Hastie, Tibshirani and Friedman, 2nd edition 2009), a popular reference book for statistics and machine learning researchers. An Introduction to Statistical Learning covers many of the same topics, but at a level accessible to a much broader audience. This book is targeted at statisticians and non-statisticians alike who wish to use cutting-edge statistical learning techniques to analyze their data. The text assumes only a previous course in linear regression and no knowledge of matrix algebra.

    It discusses new results, along with applications of probability theory to a variety of problems. The book contains many exercises and is suitable for use as a textbook on graduate-level courses involving data analysis. Aimed at readers already familiar with applied mathematics at an advanced undergraduate level or higher, it is of interest to scientists concerned with inference from incomplete information.