But many people use data in ways they don't even understand, meaning they aren't getting the most from it. Bayesian Statistics the Fun Way will change that.This book will give you a complete understanding of Bayesian statistics through simple explanations and un-boring examples. Find out the probability of UFOs landing in your garden, how likely Han Solo is to survive a flight through an asteroid shower, how to win an argument about conspiracy theories, and whether a burglary really was a burglary, to name a few examples.By using these off-the-beaten-track examples, the author actually makes learning statistics fun. And you'll learn real skills, like how to:- How to measure your own level of uncertainty in a conclusion or belief- Calculate Bayes theorem and understand what it's useful for- Find the posterior, likelihood, and prior to check the accuracy of your conclusions- Calculate distributions to see the range of your data- Compare hypotheses and draw reliable conclusions from themNext time you find yourself with a sheaf of survey results and no idea what to do with them, turn to Bayesian Statistics the Fun Way to get the most value from your data.

    The complexity of nature's shapes differs in kind, not merely degree, from that of the shapes of ordinary geometry, the geometry of fractal shapes.Now that the field has expanded greatly with many active researchers, Mandelbrot presents the definitive overview of the origins of his ideas and their new applications. The Fractal Geometry of Nature is based on his highly acclaimed earlier work, but has much broader and deeper coverage and more extensive illustrations.

    Her diary vividly captures that “gritty nightmare” as it was lived by one rural family—and by millions of other Americans. The books opens in 1927—“the last of the good years”—when Ann Marie is a teenager living with her parents, brother, and sister on a stock farm in southeastern North Dakota. We follow her family and friends, descendants of homesteaders, through the next ten years—a time of searing summer heat and desiccated fields, dying livestock, dust to the tops of fence posts and prices at rock bottom—a time when whole communities lost their homes and livelihoods to mortgages and, hardest of all, to government recovery programs. We also see the coming to maturity of the author in the face of economic hardship, frustrating family circumstances, and the stifling restrictions that society then placed on young women. Ann Marie Low’s diary, supplemented with reminiscences, offers a rich, circumstantial view of rural life a half century ago: planting and threshing before the prevalence of gasoline-powered engines, washing with rain water and ironing with sadirons, hauling coal on sleds over snow-clogged roads, going to end-of-school picnics and country dances, and hoarding the egg and cream money for college. Here, too, is an iconoclastic on-the-scene account of how a federal work project, the construction of a wildlife refuge, actually operated. Many readers will recognize parts of their own past in Ann Marie Low’s story; for others it will serve as a compelling record of the Dust Bowl experience.

    Twenty-five thousand feet above Malta--that is where the Spitfires intercepted the Messerschmitts, Macchis, and Reggianes as they swept eastward in their droves, screening the big Junkers with their bomb loads as they pummeled the island beneath: the most bombed patch of ground in the world. One of those Spitfire pilots was George Beurling, nicknamed "Screwball," who in fourteen flying days destroyed twenty-seven German and Italian aircraft and damaged many more. Hailing from Canada, Beurling finally made it to Malta in the summer of 1942 after hard training and combat across the Channel. Malta Spitfire tells his story and that of the gallant Spitfire squadron, 249, which day after day ascended to the "top of the hill" to meet the enemy against overwhelming odds. With this memoir, readers experience the sensation of being in the cockpit with him, climbing to meet the planes driving in from Sicily, diving down through the fighter screen at the bombers, dodging the bullets coming out of the sun, or whipping up under the belly of an Me for a deflection shot at the engine. This is war without sentiment or romance, told in terms of human courage, skill, and heroism--a classic of WWII military aviation.

    It’s a tale of success--and excess: great music, drugs (he overdosed and was pronounced dead three times), and resurrection.The Dead Boys, with roots in the band Rocket from the Tombs, came out of Cleveland to dominate the NYC punk scene in the mid-1970s. Their hit “Sonic Reducer” soon became a punk anthem. Now, for the first time, Cheetah dishes on the people he’s known onstage and off, including the Dead Boys’ legendary singer Stiv Bators, Johnny Thunders of the New York Dolls, the Ramones, the Clash, Pere Ubu, and the Ghetto Dogs, as well as life at CBGBs, a year with Nico, and more.Straight from the man, these are the backstage stories that every punk fan will want to hear. Never mind the Sex Pistols, here’s Cheetah Chrome!

    So are we on the edge of an AI-pocalypse, with super-intelligent devices superseding humanity, as predicted by Stephen Hawking? Or will this herald a kind of Utopia, with machines doing a far better job at complex tasks than us? You might not realise it, but you interact with AIs every day. They route your phone calls, approve your credit card transactions and help your doctor interpret results. Driverless cars will soon be on the roads with a decision-making computer in charge. But how do machines actually think and learn? In Machines That Think, AI experts and New Scientist explore how artificial ingence helps us understand human intelligence, machines that compose music and write stories - and ask if AI is really a threat.--

    He traces AI's origins in ancient myth, through literary classics like Frankenstein, to today's sci-fi blockbusters, arguing that a fascination with AI is hardwired into the human psyche. He explains AI's history, technology, and potential; its manifestations in intelligent machines; its connections to neurology and consciousness, as well as—perhaps most tellingly—what AI reveals about us as human beings.In Our Own Image argues that we are on the brink of a fourth industrial revolution—poised to enter the age of Artificial Intelligence as science fiction becomes science fact. Ultimately, Zarkadakis observes, the fate of AI has profound implications for the future of science and humanity itself.

    What will happen when misinformation moves from our social media feeds into our everyday lives?Online disinformation stormed our political process in 2016 and has only worsened since. Yet as Samuel Woolley shows in this urgent book, it may pale in comparison to what's to come: humanlike automated voice systems, machine learning, "deepfake" AI-edited videos and images, interactive memes, virtual reality, and more. These technologies have the power not just to manipulate our politics, but to make us doubt our eyes and ears and even feelings.Deeply researched and compellingly written, The Reality Game describes the profound impact these technologies will have on our lives. Each new invention built without regard for its consequences edges us further into this digital dystopia.Yet Woolley does not despair. Instead, he argues pointedly for a new culture of innovation, one built around accountability and especially transparency. With social media dragging us into a never-ending culture war, we must learn to stop fighting and instead prevent future manipulation. This book shows how we can use our new tools not to control people but to empower them.

    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.

    On Lisp explains the reasons behind Lisp's growing popularity as a mainstream programming language. On Lisp is a comprehensive study of advanced Lisp techniques, with bottom-up programming as the unifying theme. It gives the first complete description of macros and macro applications. The book also covers important subjects related to bottom-up programming, including functional programming, rapid prototyping, interactive development, and embedded languages. The final chapter takes a deeper look at object-oriented programming than previous Lisp books, showing the step-by-step construction of a working model of the Common Lisp Object System (CLOS). As well as an indispensable reference, On Lisp is a source of software. Its examples form a library of functions and macros that readers will be able to use in their own Lisp programs.

    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.

    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.

    During that week, you will learn almost everything modern machine learning has to offer. The author and other practitioners have spent years learning these concepts.Companion wiki — the book has a continuously updated wiki that extends some book chapters with additional information: Q&A, code snippets, further reading, tools, and other relevant resources.Flexible price and formats — choose from a variety of formats and price options: Kindle, hardcover, paperback, EPUB, PDF. If you buy an EPUB or a PDF, you decide the price you pay!Read first, buy later — download book chapters for free, read them and share with your friends and colleagues. Only if you liked the book or found it useful in your work, study or business, then buy it.

    Can you imagine Draupadi in a Brothel house? How did she end there and who is responsible for that? Read this short story to meet her and know about her life.

    Managers and entrepreneurs in the digital era must learn to live in two worlds—the conventional economy and the platform economy. Platforms that operate for business purposes usually exist at the level of an industry or ecosystem, bringing together individuals and organizations so they can innovate and interact in ways not otherwise possible. Platforms create economic value far beyond what we see in conventional companies.The Business of Platforms is an invaluable, in-depth look at platform strategy and digital innovation. Cusumano, Gawer, and Yoffie address how a small number of companies have come to exert extraordinary influence over every dimension of our personal, professional, and political lives. They explain how these new entities differ from the powerful corporations of the past. They also question whether there are limits to the market dominance and expansion of these digital juggernauts. Finally, they discuss the role governments should play in rethinking data privacy laws, antitrust, and other regulations that could reign in abuses from these powerful businesses.Their goal is to help managers and entrepreneurs build platform businesses that can stand the test of time and win their share of battles with both digital and conventional competitors. As experts who have studied and worked with these firms for some thirty years, this book is the most authoritative and timely investigation yet of the powerful economic and technological forces that make platform businesses, from Amazon and Apple to Microsoft, Facebook, and Google—all dominant players in shaping the global economy, the future of work, and the political world we now face.