You will spoil the story and your own enjoyment if you read a series in the wrong order and you will miss the development of an author’s writing if you read their books in a helter-skelter fashion. With our original reading list you get the perfect tool to enjoy C. J. Box’s books the way they where meant to be enjoyed. You can also use the reading list as checklist. Simply use the inbuilt highlight feature to highlight all the books that you have already read. Inside this book you will find a link that will allow you to download three classics for FREE along with three free audiobooks. Enjoy! Note To Readers This is a bibliography. The author and publisher of this book do not guarantee the accuracy and/or completeness of the content within this book and are not liable for damages arising from the use of this book. The bibliography portion of this book can be found in publicly available sources and only includes elements, such as titles and dates of publication, which are not subject to copyright protection. The bibliography is unofficial and not approved, authorized, licensed, or endorsed by any author, publisher, or organization mentioned within it.

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    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.

     Neural networks are a key element of deep learning and artificial intelligence, which today is capable of some truly impressive feats. Yet too few really understand how neural networks actually work. This guide will take you on a fun and unhurried journey, starting from very simple ideas, and gradually building up an understanding of how neural networks work. You won't need any mathematics beyond secondary school, and an accessible introduction to calculus is also included. The ambition of this guide is to make neural networks as accessible as possible to as many readers as possible - there are enough texts for advanced readers already! You'll learn to code in Python and make your own neural network, teaching it to recognise human handwritten numbers, and performing as well as professionally developed networks. Part 1 is about ideas. We introduce the mathematical ideas underlying the neural networks, gently with lots of illustrations and examples. Part 2 is practical. We introduce the popular and easy to learn Python programming language, and gradually builds up a neural network which can learn to recognise human handwritten numbers, easily getting it to perform as well as networks made by professionals. Part 3 extends these ideas further. We push the performance of our neural network to an industry leading 98% using only simple ideas and code, test the network on your own handwriting, take a privileged peek inside the mysterious mind of a neural network, and even get it all working on a Raspberry Pi. All the code in this has been tested to work on a Raspberry Pi Zero.

    It's all muscle and no fluff. Discover how the experts tackle Windows 7--and challenge yourself to new levels of mastery! Compare features and capabilities in each edition of Windows 7.Configure and customize your system with advanced setup options.Manage files, folders, and media libraries.Set up a wired or wireless network and manage shared resources.Administer accounts, passwords, and logons--and help control access to resources.Configure Internet Explorer 8 settings and security zones.Master security essentials to help protect against viruses, worms, and spyware.Troubleshoot errors and fine-tune performance.Automate routine maintenance with scripts and other tools. CD includes: Fully searchable eBookDownloadable gadgets and other tools for customizing Windows 7Insights direct from the product team on the official Windows 7 blogLinks to the latest security updates and products, demos, blogs, and user communities For customers who purchase an ebook version of this title, instructions for downloading the CD files can be found in the ebook.

    This book provides the design strategy and tactics you need to integrate game mechanics into any kind of consumer-facing website or mobile app. Learn how to use core game concepts, design patterns, and meaningful code samples to a create fun and captivating social environment.Whether you're an executive, developer, producer, or product specialist, Gamification by Design will show you how game mechanics can help you build customer loyalty.Discover the motivational framework game designers use to segment and engage consumersUnderstand core game mechanics such as points, badges, levels, challenges, and leaderboardsEngage your consumers with reward structures, positive reinforcement, and feedback loopsCombine game mechanics with social interaction for activities such as collecting, gifting, heroism, and statusDive into case studies on Nike and Yahoo!, and analyze interactions at Google, Facebook, and ZyngaGet the architecture and code to gamify a basic consumer site, and learn how to use mainstream gamification APIs from Badgeville"Turning applications into games is a huge trend. This book does a great job of identifying the core lasting principals you need to inspire your users to visit again and again." —Adam Loving Freelance Social Game Developer and founder of Twibes Twitter Groups

    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.

    For anyone who has ever wondered how computers solve problems, an engagingly written guide for nonexperts to the basics of computer algorithms.

    The framework of probabilistic graphical models, presented in this book, provides a general approach for this task. The approach is model-based, allowing interpretable models to be constructed and then manipulated by reasoning algorithms. These models can also be learned automatically from data, allowing the approach to be used in cases where manually constructing a model is difficult or even impossible. Because uncertainty is an inescapable aspect of most real-world applications, the book focuses on probabilistic models, which make the uncertainty explicit and provide models that are more faithful to reality.Probabilistic Graphical Models discusses a variety of models, spanning Bayesian networks, undirected Markov networks, discrete and continuous models, and extensions to deal with dynamical systems and relational data. For each class of models, the text describes the three fundamental cornerstones: representation, inference, and learning, presenting both basic concepts and advanced techniques. Finally, the book considers the use of the proposed framework for causal reasoning and decision making under uncertainty. The main text in each chapter provides the detailed technical development of the key ideas. Most chapters also include boxes with additional material: skill boxes, which describe techniques; case study boxes, which discuss empirical cases related to the approach described in the text, including applications in computer vision, robotics, natural language understanding, and computational biology; and concept boxes, which present significant concepts drawn from the material in the chapter. Instructors (and readers) can group chapters in various combinations, from core topics to more technically advanced material, to suit their particular needs.

    The award-winning author Melanie Mitchell, a leading computer scientist, now reveals AI’s turbulent history and the recent spate of apparent successes, grand hopes, and emerging fears surrounding it.In Artificial Intelligence, Mitchell turns to the most urgent questions concerning AI today: How intelligent—really—are the best AI programs? How do they work? What can they actually do, and when do they fail? How humanlike do we expect them to become, and how soon do we need to worry about them surpassing us? Along the way, she introduces the dominant models of modern AI and machine learning, describing cutting-edge AI programs, their human inventors, and the historical lines of thought underpinning recent achievements. She meets with fellow experts such as Douglas Hofstadter, the cognitive scientist and Pulitzer Prize–winning author of the modern classic Gödel, Escher, Bach, who explains why he is “terrified” about the future of AI. She explores the profound disconnect between the hype and the actual achievements in AI, providing a clear sense of what the field has accomplished and how much further it has to go.Interweaving stories about the science of AI and the people behind it, Artificial Intelligence brims with clear-sighted, captivating, and accessible accounts of the most interesting and provocative modern work in the field, flavored with Mitchell’s humor and personal observations. This frank, lively book is an indispensable guide to understanding today’s AI, its quest for “human-level” intelligence, and its impact on the future for us all.

    

    She claimed the police had inserted a stick inside her… Swaranpreet realised that she had been cruelly violated; He spoke a single sentence but repeated it twice in chaste Punjabi: ‘Please give me a turban? I want nothing else…’ These are voices begging for deliverance in the aftermath of Indira Gandhi’s assassination in October-November 1984 in which 2,733 Sikhs were killed, burnt and exterminated by lumpens in the country. Nilanjan Mukhopadhyay walks us through one of the most shameful episodes of sectarian violence in post Independent India and highlights the apathy of subsequent governments towards Sikhs who paid a price for what was clearly a state-sponsored riot. Poignant, raw and most importantly, macabre, the personal histories in the book reveal how even after three decades, a community continues to battle for its identity in its own country.

    Their determination to meet their goals and the challenges they overcame to succeed, make their stories unique and inspirational.Elon Musk is known for thinking outside the box, dreaming big and working tirelessly to achieve those dreams. He is open to ideas and ways to collaborate and improve what he is working on while funding these solutions. He thrives on his passion for work and is willing to put his weight behind projects he believes in and the innovations coming out of Space X and Tesla provide ample proof.Discover this maverick’s story and how you could emulate him!George Ilian has made his mark on the digital industry, owning an ebook business among other endeavours. He is the author of 18 books in the genre of business and motivation.