Special features designed to speak to a woman’s heart appear throughout the Bible text, revealing Scripture-based insights about how godly womanhood grows from a woman’s identity as a Christ-follower and a child of the Kingdom. Now with a beautiful full-color redesign, The Woman’s Study Bible reflects the contributions of over 80 women from a wide variety of ethnic, denominational, educational, and occupational backgrounds. Since the publication of the first edition of The Woman’s Study Bible under the editorial guidance of Dorothy Kelley Patterson and Rhonda Harrington Kelley, this landmark study Bible has sold over 1.5 million copies. Features Include: Beautiful full-color design throughout Detailed biographical portraits of over 100 biblical women Thousands of extensive verse-by-verse study notes Over 300 in-text topical articles on relevant issues Insightful essays by women who are recognized experts in the fields of theology, biblical studies, archaeology, and philosophy Book introductions and outlines Hundreds of full-color in-text maps, charts, timelines, and family trees Quotes from godly women throughout history Set of full-page maps of the biblical world Topical index Concordance 10.5-point print size

    With Step By Step, you set the pace-building and practicing the skills you need, just when you need them! Topics include building a project plan and fine-tuning the details; scheduling tasks, assigning resources, and managing dependencies; monitoring progress and costs; keeping projects on track; communicating project data through Gantt charts and other views.

    The candidness of the author’s voice, the gentle humour of fleeting narrative and the fragility of diary entries, photographs, collages and sketches will make My Daughters’ Mum resonate with every reader.

    The Computer Revolution may be even more life-changing than the Industrial Revolution. We can do things with computers that could never be done before, and computers can do things for us that could never be done before.But our love of computers should not cloud our thinking about their limitations.We are told that computers are smarter than humans and that data mining can identify previously unknown truths, or make discoveries that will revolutionize our lives. Our lives may well be changed, but not necessarily for the better. Computers are very good at discovering patterns, but are uselessin judging whether the unearthed patterns are sensible because computers do not think the way humans think.We fear that super-intelligent machines will decide to protect themselves by enslaving or eliminating humans. But the real danger is not that computers are smarter than us, but that we think computers are smarter than us and, so, trust computers to make important decisions for us.The AI Delusion explains why we should not be intimidated into thinking that computers are infallible, that data-mining is knowledge discovery, and that black boxes should be trusted.

    This volume is comprised of the years 1900 through 1959, and includes all of the best known cases of the period, as well as many more lesser-known murders, all presented in a compelling chronological narrative that takes the reader on a grisly journey through the blood-soaked avenues of early twentieth century crime. Featuring: The Peasenhall Murder. The Seal Chart Murder. The Atlanta Ripper. The Villisca Axe Murders. The Axeman of New Orleans. The Green Bicycle Case. Little Lord Fauntleroy. Hinterkaifeck Farm. The St. Aubin Street Massacre. The Wallace Case. The Atlas Vampire. The Brighton Trunk Crime. The Cleveland Torso Murderer. The Horror in Room 1046. Who Put Bella in the Wych Elm? The Pitchfork Murder. The Sodder Children. The Phantom Killer. The Black Dahlia. Somerton Man. The Grimes Sisters. The Boy in the Box. And Much More!

     Are you stuck playing in lousy tournaments versus lousy opponents? Do you still lose even at the smallest stakes? It’s time to master small stakes poker with a world-class poker champion as your guide. Jonathan Little has cashed for more than $6.4 million in live poker tournaments. This two-time World Poker Tour champion knows that to make millions on poker’s biggest stage, you have to start small. Through his revolutionary approach, you can finally master the skills it takes to win small stakes tournaments. By learning how to crush your small stakes opponents, you’ll be prepared to handle bigger challenges -- and bigger cash outs -- that come with larger buy-in tournaments. In Strategies for Beating Small Stakes Poker Tournaments, you’ll discover: - Skills that will instantly improve your poker game - How to stop blaming bad luck and learn the real secret to mastering poker - The pitfalls to avoid that will allow you to crush your mistake-prone peers - The reasons pros make the plays they make and how you can adopt their methods - How to win more money from poker, and much, much more! When you learn to think for yourself and adjust your strategy based on specific opponents, you’ll start to turn your game around. As the author of 11 best-selling poker books, Little can show you exactly how to learn these skills and win much more money in the long term. If you like concise, actionable information you can start using right away, then you’ll love Little’s game-changing poker guide. Buy the book to start cashing out today!

    It is also a practical, modern introduction to scientific computing in Python, tailored for data-intensive applications. This is a book about the parts of the Python language and libraries you'll need to effectively solve a broad set of data analysis problems. This book is not an exposition on analytical methods using Python as the implementation language.Written by Wes McKinney, the main author of the pandas library, this hands-on book is packed with practical cases studies. It's ideal for analysts new to Python and for Python programmers new to scientific computing.Use the IPython interactive shell as your primary development environmentLearn basic and advanced NumPy (Numerical Python) featuresGet started with data analysis tools in the pandas libraryUse high-performance tools to load, clean, transform, merge, and reshape dataCreate scatter plots and static or interactive visualizations with matplotlibApply the pandas groupby facility to slice, dice, and summarize datasetsMeasure data by points in time, whether it's specific instances, fixed periods, or intervalsLearn how to solve problems in web analytics, social sciences, finance, and economics, through detailed examples

    It is a ‘ must’ book for those in every branch of science . . . in addition, economists, politicians, statesmen, and businessmen cannot afford to overlook cybernetics and its tremendous, even terrifying implications. "It is a beautifully written book, lucid, direct, and despite its complexity, as readable by the layman as the trained scientist." -- John B. Thurston, "The Saturday Review of Literature" Acclaimed one of the "seminal books . . . comparable in ultimate importance to . . . Galileo or Malthus or Rousseau or Mill," "Cybernetics" was judged by twenty-seven historians, economists, educators, and philosophers to be one of those books published during the "past four decades", which may have a substantial impact on public thought and action in the years ahead." -- Saturday Review

    For the singles who are searching of love, how do you know if you are ready to commit yourself to a relationship.For those who are in a relationship, is there a method to make the romantic relationship last?Is love a matter of the heart and emotions, or can love be managed in a logical and practical manner?**In Unbreakable, Aiman Azlan encourages us to change our perspective when it comes to love and relationship — that it is not something you fall into but something that you need to prepare and work for.Not only does this book provides practical steps towards managing love, but it also guides readers on how to embrace and navigate the emotional aspect of interhuman relationship."You will never be ready to be in a committed relationship, but you can always be prepared."

    Several resources exist for individual pieces of this data science stack, but only with the Python Data Science Handbook do you get them all—IPython, NumPy, Pandas, Matplotlib, Scikit-Learn, and other related tools.Working scientists and data crunchers familiar with reading and writing Python code will find this comprehensive desk reference ideal for tackling day-to-day issues: manipulating, transforming, and cleaning data; visualizing different types of data; and using data to build statistical or machine learning models. Quite simply, this is the must-have reference for scientific computing in Python.With this handbook, you’ll learn how to use: * IPython and Jupyter: provide computational environments for data scientists using Python * NumPy: includes the ndarray for efficient storage and manipulation of dense data arrays in Python * Pandas: features the DataFrame for efficient storage and manipulation of labeled/columnar data in Python * Matplotlib: includes capabilities for a flexible range of data visualizations in Python * Scikit-Learn: for efficient and clean Python implementations of the most important and established machine learning algorithms

    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.

    But in spirit, the title is apt, as the book does cover a much broader range of topics than a typical introductory book on mathematical statistics. This book is for people who want to learn probability and statistics quickly. It is suitable for graduate or advanced undergraduate students in computer science, mathematics, statistics, and related disciplines. The book includes modern topics like nonparametric curve estimation, bootstrapping, and clas- sification, topics that are usually relegated to follow-up courses. The reader is presumed to know calculus and a little linear algebra. No previous knowledge of probability and statistics is required. Statistics, data mining, and machine learning are all concerned with collecting and analyzing data. For some time, statistics research was con- ducted in statistics departments while data mining and machine learning re- search was conducted in computer science departments. Statisticians thought that computer scientists were reinventing the wheel. Computer scientists thought that statistical theory didn't apply to their problems. Things are changing. Statisticians now recognize that computer scientists are making novel contributions while computer scientists now recognize the generality of statistical theory and methodology. Clever data mining algo- rithms are more scalable than statisticians ever thought possible. Formal sta- tistical theory is more pervasive than computer scientists had realized.

    The Tour is home to rowdy, randy young men often drunk with money and fame; fueled by alcohol and adrenaline, they barnstorm from town to town like rock stars, with all the attendant excesses. And in each player's shadow is his faithful caddie -- performing a thankless six-figure job that comes with all the security of a handshake deal. The PGA Tour offers fabulous rewards, but its good life does not come without a price. In Bud, Sweat, and Tees, Alan Shipnuck takes a no-holds-barred look at modern professional golf. Rich Beem, the hero of our story, joined the Tour as the most clueless of rookies, a logo-free rube only a couple of years removed from the straight world, where he made seven dollars an hour hawking cell phones. Beem took his winnings from big-money matches all across the state of Texas and scraped together enough to go out on Tour, but as he would quickly find out, getting to the big leagues is only half the battle. The fun-loving Beem, more likely to pound beers than range balls, first struggled to fit in among the country-club brats who populate the pro golf scene, and then had to fight to survive the cutthroat competition and crushing self-doubt. Staying true to his girl back home would prove equally challenging.Meanwhile, Steve Duplantis, the one-time golden boy of the Tour's caddie ranks, was enduring his own tribulations. At the tender age of twenty-one Duplantis began packing for Jim Furyk, and together they reached the pinnacle of the golf world, from Ryder Cup dustups to near misses at the Masters. But like Beem, Duplantis has a taste for thewild life, which helps explain how he wound up as a single dad, trying to balance the demands of fatherhood with the siren song of the road -- a juggling act that eventually cost him his lucrative job on Furyk's bag. Fate brought Duplantis and Beem together, and in their first tournament, the Kemper Open, they pulled off one of the most improbable triumphs in golf history.What happens next, at this unlikely intersection of lives and careers? How does a lifelong underdog like Beem handle overnight fame and fortune? Would Duplantis make good on this second chance and turn his career, and maybe his life, around? And would Beem and Duplantis's partnership survive the course of a turbulent season chock full of enough misadventures to land them in a Scottish jail?"Bud, Sweat, and Tees" is a sometimes bawdy, often hilarious, and always unpredictable account of a strange and magical year in the lives, on and off the course, of golfer and caddie. An exciting and often poignant story, it stands as the best insider's sports book since Jim Bouton's Ball Four, and marks Alan Shipnuck as a writer of extraordinary promise.

    You'll progress from a complete beginner regarding Arduino programming and electronics knowledge to intermediate skills and the confidence to create your own amazing Arduino projects. Absolutely no experience in programming or electronics required!Rather than requiring you to wade through pages of theory before you start making things, this book has a hands-on approach. You will dive into making projects right from the start, learning how to use various electronic components and how to program the Arduino to control or communicate with those components.Each project is designed to build upon the knowledge learned in earlier projects and to further your knowledge in programming as well as skills with electronics. By the end of the book you will be able create your own projects confidently and with creativity.Please note: the print version of this title is black & white; the eBook is full color. You can download the color diagrams in the book from http: //www.apress.com/9781430232407

    Their discussion ranges from the history of the field's intellectual foundations to the most recent developments and applications.Reinforcement learning, one of the most active research areas in artificial intelligence, is a computational approach to learning whereby an agent tries to maximize the total amount of reward it receives when interacting with a complex, uncertain environment. In Reinforcement Learning, Richard Sutton and Andrew Barto provide a clear and simple account of the key ideas and algorithms of reinforcement learning. Their discussion ranges from the history of the field's intellectual foundations to the most recent developments and applications. The only necessary mathematical background is familiarity with elementary concepts of probability.The book is divided into three parts. Part I defines the reinforcement learning problem in terms of Markov decision processes. Part II provides basic solution methods: dynamic programming, Monte Carlo methods, and temporal-difference learning. Part III presents a unified view of the solution methods and incorporates artificial neural networks, eligibility traces, and planning; the two final chapters present case studies and consider the future of reinforcement learning.