Artificial Intelligence is the most exciting technology of the century, and Deep Learning is, quite literally, the "brain" behind the world's smartest Artificial Intelligence systems out there. Grokking Deep Learning is the perfect place to begin the deep learning journey. Rather than just learning the "black box" API of some library or framework, readers will actually understand how to build these algorithms completely from scratch. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.
With the reinvigoration of neural networks in the 2000s, deep learning has become an extremely active area of research, one that’s paving the way for modern machine learning. In this practical book, author Nikhil Buduma provides examples and clear explanations to guide you through major concepts of this complicated field. Companies such as Google, Microsoft, and Facebook are actively growing in-house deep-learning teams. For the rest of us, however, deep learning is still a pretty complex and difficult subject to grasp. If you’re familiar with Python, and have a background in calculus, along with a basic understanding of machine learning, this book will get you started. Examine the foundations of machine learning and neural networks Learn how to train feed-forward neural networks Use TensorFlow to implement your first neural network Manage problems that arise as you begin to make networks deeper Build neural networks that analyze complex images Perform effective dimensionality reduction using autoencoders Dive deep into sequence analysis to examine language Learn the fundamentals of reinforcement learning
Summary Grokking Algorithms is a fully illustrated, friendly guide that teaches you how to apply common algorithms to the practical problems you face every day as a programmer. You'll start with sorting and searching and, as you build up your skills in thinking algorithmically, you'll tackle more complex concerns such as data compression and artificial intelligence. Each carefully presented example includes helpful diagrams and fully annotated code samples in Python. Learning about algorithms doesn't have to be boring! Get a sneak peek at the fun, illustrated, and friendly examples you'll find in Grokking Algorithms on Manning Publications' YouTube channel. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the Technology An algorithm is nothing more than a step-by-step procedure for solving a problem. The algorithms you'll use most often as a programmer have already been discovered, tested, and proven. If you want to understand them but refuse to slog through dense multipage proofs, this is the book for you. This fully illustrated and engaging guide makes it easy to learn how to use the most important algorithms effectively in your own programs. About the Book Grokking Algorithms is a friendly take on this core computer science topic. In it, you'll learn how to apply common algorithms to the practical programming problems you face every day. You'll start with tasks like sorting and searching. As you build up your skills, you'll tackle more complex problems like data compression and artificial intelligence. Each carefully presented example includes helpful diagrams and fully annotated code samples in Python. By the end of this book, you will have mastered widely applicable algorithms as well as how and when to use them. What's Inside Covers search, sort, and graph algorithms Over 400 pictures with detailed walkthroughs Performance trade-offs between algorithms Python-based code samples About the Reader This easy-to-read, picture-heavy introduction is suitable for self-taught programmers, engineers, or anyone who wants to brush up on algorithms. About the Author Aditya Bhargava is a Software Engineer with a dual background in Computer Science and Fine Arts. He blogs on programming at adit.io. Table of Contents Introduction to algorithms Selection sort Recursion Quicksort Hash tables Breadth-first search Dijkstra's algorithm Greedy algorithms Dynamic programming K-nearest neighbors
Author: Casper Harteveld
Publisher: Springer Science & Business Media
Release Date: 2011-02-26
Many designers, policy makers, teachers, and other practitioners are beginning to understand the usefulness of using digital games beyond entertainment. Games have been developed for teaching, recruiting and to collect data to improve search engines. This book examines the fundamentals of designing any game with a serious purpose and provides a way of thinking on how to design one successfully. The reader will be introduced to a design philosophy called “Triadic Game Design.”; a theory that all games involve three worlds: the worlds of Reality, Meaning, and Play. Each world is affiliated with aspects. A balance needs to be found within and between the three worlds. Such a balance is difficult to achieve, during the design many tensions will arise, forcing designers to make trade-offs. To deal with these tensions and to ensure that the right decisions are made to create a harmonic game, a frame of reference is needed. This is what Triadic Game Design offers.
Deep learning is applicable to a widening range of artificial intelligence problems, such as image classification, speech recognition, text classification, question answering, text-to-speech, and optical character recognition. Deep Learning with Python is structured around a series of practical code examples that illustrate each new concept introduced and demonstrate best practices. By the time you reach the end of this book, you will have become a Keras expert and will be able to apply deep learning in your own projects. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.
Author: Robert A. Heinlein
Publisher: Hachette UK
Release Date: 2014-06-05
The original uncut edition of STRANGER IN A STRANGE LAND by Hugo Award winner Robert A Heinlein - one of the most beloved, celebrated science-fiction novels of all time. Epic, ambitious and entertaining, STRANGER IN A STRANGE LAND caused controversy and uproar when it was first published and is still topical and challenging today. Twenty-five years ago, the first manned mission to Mars was lost, and all hands presumed dead. But someone survived... Born on the doomed spaceship and raised by the Martians who saved his life, Valentine Michael Smith has never seen a human being until the day a second expedition to Mars discovers him. Upon his return to Earth, a young nurse named Jill Boardman sneaks into Smith's hospital room and shares a glass of water with him, a simple act for her but a sacred ritual on Mars. Now, connected by an incredible bond, Smith, Jill and a writer named Jubal must fight to protect a right we all take for granted: the right to love.
Unlock deeper insights into Machine Leaning with this vital guide to cutting-edge predictive analytics About This Book Leverage Python's most powerful open-source libraries for deep learning, data wrangling, and data visualization Learn effective strategies and best practices to improve and optimize machine learning systems and algorithms Ask – and answer – tough questions of your data with robust statistical models, built for a range of datasets Who This Book Is For If you want to find out how to use Python to start answering critical questions of your data, pick up Python Machine Learning – whether you want to get started from scratch or want to extend your data science knowledge, this is an essential and unmissable resource. What You Will Learn Explore how to use different machine learning models to ask different questions of your data Learn how to build neural networks using Keras and Theano Find out how to write clean and elegant Python code that will optimize the strength of your algorithms Discover how to embed your machine learning model in a web application for increased accessibility Predict continuous target outcomes using regression analysis Uncover hidden patterns and structures in data with clustering Organize data using effective pre-processing techniques Get to grips with sentiment analysis to delve deeper into textual and social media data In Detail Machine learning and predictive analytics are transforming the way businesses and other organizations operate. Being able to understand trends and patterns in complex data is critical to success, becoming one of the key strategies for unlocking growth in a challenging contemporary marketplace. Python can help you deliver key insights into your data – its unique capabilities as a language let you build sophisticated algorithms and statistical models that can reveal new perspectives and answer key questions that are vital for success. Python Machine Learning gives you access to the world of predictive analytics and demonstrates why Python is one of the world's leading data science languages. If you want to ask better questions of data, or need to improve and extend the capabilities of your machine learning systems, this practical data science book is invaluable. Covering a wide range of powerful Python libraries, including scikit-learn, Theano, and Keras, and featuring guidance and tips on everything from sentiment analysis to neural networks, you'll soon be able to answer some of the most important questions facing you and your organization. Style and approach Python Machine Learning connects the fundamental theoretical principles behind machine learning to their practical application in a way that focuses you on asking and answering the right questions. It walks you through the key elements of Python and its powerful machine learning libraries, while demonstrating how to get to grips with a range of statistical models.
ReactiveX is the common tongue of the reactive programming world. It's a new programming paradigm applied to infinite scenarios using any popular programming language. To really understand Rx, you need to rewire your brain to see the world differently, and we're here to help you. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. Grokking ReactiveX is a practical book that teaches readers how to solve complex problems elegantly and with few lines of code. To make learning easier, each chapter introduces a new concept and then immediately demonstrates how to use it. Following carefully-selected examples with thorough, well-paced explanations, readers will immerse themselves in ReactiveX, concept by concept.
Now in full color, the 10th anniversary edition of this classic book takes you deep into the influences that underlie modern video games, and examines the elements they share with traditional games such as checkers. At the heart of his exploration, veteran game designer Raph Koster takes a close look at the concept of fun and why it’s the most vital element in any game. Why do some games become boring quickly, while others remain fun for years? How do games serve as fundamental and powerful learning tools? Whether you’re a game developer, dedicated gamer, or curious observer, this illustrated, fully updated edition helps you understand what drives this major cultural force, and inspires you to take it further. You’ll discover that: Games play into our innate ability to seek patterns and solve puzzles Most successful games are built upon the same elements Slightly more females than males now play games Many games still teach primitive survival skills Fictional dressing for modern games is more developed than the conceptual elements Truly creative designers seldom use other games for inspiration Games are beginning to evolve beyond their prehistoric origins
Author: Aaron Dignan
Publisher: Simon and Schuster
Release Date: 2011-03-08
Genre: Business & Economics
Ever wonder why teens can spend entire weekends playing video games but struggle with just one hour of homework? Why we’re addicted to certain websites and steal glances at our smartphones under the dinner table? Or why some people are able to find joy in difficult or repetitive jobs while others burn out? It’s not the experiences themselves but the way they’re structured that matters. All our lives we’ve been told that games are distractions—playful pastimes, but unrelated to success. In Game Frame, Aaron Dignan shows us that the opposite is true: games produce peak learning conditions and accelerated achievement. Here, the crucial connection between the games we love to play and the everyday tasks, goals, and dreams we have trouble realizing is illuminated. Aaron Dignan is the thirty-something founder of a successful digital strategy firm that studies the transformative power of technology in culture. He and his peers were raised on a steady diet of games and gadgets, ultimately priming them to challenge the status quo of the modern workplace. What they learned from games goes deeper than hand-eye coordination; instead, this generation intrinsically understands the value of adding the elements of games into everyday life. Game Frame is the first prescriptive explanation of what games mean to us, the human psychology behind their magnetic pull, and how we can use the lessons they teach as a framework to achieve our potential in business and beyond. Games are a powerful way to influence and change behavior in any setting. Here, Dignan outlines why games and play are such important trends in culture today, and how our technology, from our iPhones to our hybrid cars, primes us to be instinctive players. Game Frame tackles the challenging task of defining games and the mechanics that make games work from several perspectives, then explores these ideas through the lens of neuroscience. Finally, Dignan provides practical tips for using basic game mechanics in a variety of settings, such as motivating employees at work or encouraging children at home, giving readers the tools to develop their own games to solve problems in their everyday lives. Illuminated throughout with a series of real-world examples and hypothetical scenarios, Game Frame promises a crash course in game design and behavioral psychology that will leave the reader—and, by extension, the world itself—more productive. Revolutionary, visionary, practical, and time-tested, Game Frame will change the way you approach life.
Author: John Paul Mueller
Publisher: John Wiley & Sons
Release Date: 2017-04-11
Discover how algorithms shape and impact our digital world All data, big or small, starts with algorithms. Algorithms are mathematical equations that determine what we see—based on our likes, dislikes, queries, views, interests, relationships, and more—online. They are, in a sense, the electronic gatekeepers to our digital, as well as our physical, world. This book demystifies the subject of algorithms so you can understand how important they are business and scientific decision making. Algorithms for Dummies is a clear and concise primer for everyday people who are interested in algorithms and how they impact our digital lives. Based on the fact that we already live in a world where algorithms are behind most of the technology we use, this book offers eye-opening information on the pervasiveness and importance of this mathematical science—how it plays out in our everyday digestion of news and entertainment, as well as in its influence on our social interactions and consumerism. Readers even learn how to program an algorithm using Python! Become well-versed in the major areas comprising algorithms Examine the incredible history behind algorithms Get familiar with real-world applications of problem-solving procedures Experience hands-on development of an algorithm from start to finish with Python If you have a nagging curiosity about why an ad for that hammock you checked out on Amazon is appearing on your Facebook page, you'll find Algorithm for Dummies to be an enlightening introduction to this integral realm of math, science, and business.
Although interest in machine learning has reached a high point, lofty expectations often scuttle projects before they get very far. How can machine learning—especially deep neural networks—make a real difference in your organization? This hands-on guide not only provides the most practical information available on the subject, but also helps you get started building efficient deep learning networks. Authors Adam Gibson and Josh Patterson provide theory on deep learning before introducing their open-source Deeplearning4j (DL4J) library for developing production-class workflows. Through real-world examples, you’ll learn methods and strategies for training deep network architectures and running deep learning workflows on Spark and Hadoop with DL4J. Dive into machine learning concepts in general, as well as deep learning in particular Understand how deep networks evolved from neural network fundamentals Explore the major deep network architectures, including Convolutional and Recurrent Learn how to map specific deep networks to the right problem Walk through the fundamentals of tuning general neural networks and specific deep network architectures Use vectorization techniques for different data types with DataVec, DL4J’s workflow tool Learn how to use DL4J natively on Spark and Hadoop
Author: Iwan Rhys Morus
Publisher: Oxford University Press
Release Date: 2017-06-08
The Oxford Illustrated History of Science is the first ever fully illustrated global history of science, from Aristotle to the atom bomb - and beyond. The first part of the book tells the story of science in both East and West from antiquity to the Enlightenment: from the ancient Mediterranean world to ancient China; from the exchanges between Islamic and Christian scholars in the Middle Ages to the Chinese invention of gunpowder, paper, and the printing press; from the Scientific Revolution of sixteenth and seventeenth century Europe to the intellectual ferment of the eighteenth century. The chapters that follow focus on the increasingly specialized story of science since end of the eighteenth century, covering experimental science in the laboratory from Michael Faraday to CERN; the exploration of nature, from intrepid Victorian explorers to twentieth century primatologists; the mapping of the universe, from the discovery of Uranus to Big Bang theory; the impact of evolutionary ideas, from Lamarck, Darwin, and Wallace to DNA; and the story of theoretical physics, from James Clark Maxwell to Quantum Theory and beyond. A concluding chapter reflects on how scientists have communicated their work to a wider public, from the Great Exhibition of 1851 to the internet in the early twenty-first century.