It is the technology behind photo tagging systems at Facebook and Google, self-driving cars, speech recognition systems on your smartphone, and much more.In particular, Deep learning excels at solving machine perception problems: understanding the content of image data, video data, or sound data. Here's a simple example: say you have a large collection of images, and that you want tags associated with each image, for example, "dog," "cat," etc. Deep learning can allow you to create a system that understands how to map such tags to images, learning only from examples. This system can then be applied to new images, automating the task of photo tagging. A deep learning model only has to be fed examples of a task to start generating useful results on new data.

    When we use consumer products from Google, Microsoft, Facebook, Apple, or Baidu, we are often interacting with a deep learning system. In this volume in the MIT Press Essential Knowledge series, computer scientist John Kelleher offers an accessible and concise but comprehensive introduction to the fundamental technology at the heart of the artificial intelligence revolution.Kelleher explains that deep learning enables data-driven decisions by identifying and extracting patterns from large datasets; its ability to learn from complex data makes deep learning ideally suited to take advantage of the rapid growth in big data and computational power. Kelleher also explains some of the basic concepts in deep learning, presents a history of advances in the field, and discusses the current state of the art. He describes the most important deep learning architectures, including autoencoders, recurrent neural networks, and long short-term networks, as well as such recent developments as Generative Adversarial Networks and capsule networks. He also provides a comprehensive (and comprehensible) introduction to the two fundamental algorithms in deep learning: gradient descent and backpropagation. Finally, Kelleher considers the future of deep learning—major trends, possible developments, and significant challenges.

    With pedagogical use of second color, it covers devices in one place so that circuit characteristics are developed early.

    They are vehicles, a series of hypothetical, self-operating machines that exhibit increasingly intricate if not always successful or civilized behavior. Each of the vehicles in the series incorporates the essential features of all the earlier models and along the way they come to embody aggression, love, logic, manifestations of foresight, concept formation, creative thinking, personality, and free will. In a section of extensive biological notes, Braitenberg locates many elements of his fantasy in current brain research.

    With it, you'll learn how to write Python programs that work with large collections of unstructured text. You'll access richly annotated datasets using a comprehensive range of linguistic data structures, and you'll understand the main algorithms for analyzing the content and structure of written communication.Packed with examples and exercises, Natural Language Processing with Python will help you: Extract information from unstructured text, either to guess the topic or identify "named entities" Analyze linguistic structure in text, including parsing and semantic analysis Access popular linguistic databases, including WordNet and treebanks Integrate techniques drawn from fields as diverse as linguistics and artificial intelligenceThis book will help you gain practical skills in natural language processing using the Python programming language and the Natural Language Toolkit (NLTK) open source library. If you're interested in developing web applications, analyzing multilingual news sources, or documenting endangered languages -- or if you're simply curious to have a programmer's perspective on how human language works -- you'll find Natural Language Processing with Python both fascinating and immensely useful.

    It describes conventional and unconventional methods of electricity generation and its economics, distribution methods, substation location, electric drives, high frequency power for induction and heating, illumination engineering, and electric traction. Each chapter contains illustrative worked problems, exercises (some with answers) and a bibliography.

    Create a tiny virtual environment, called a container, for your application that includes only its particular set of dependencies. The Docker engine accounts for, manages, and builds these containers through functionality provided by the host operating system. Software running inside containers share the Linux OS and other resources, such as libraries, making their footprints radically smaller, and the containerized applications are easy to install, manage, and remove. Developers can package their applications without worrying about environment-specific deployment concerns, and the operations team gets cleaner, more efficient systems across the board. Better still, Docker is free and open source.Docker in Action teaches readers how to create, deploy, and manage applications hosted in Docker containers. The book starts with a clear explanation of the Docker model of virtualization, comparing this approach to the traditional hypervisor model. Developers will learn how to package applications in containers, including specific techniques for testing and distributing applications via Docker Hub and other registries. Readers will learn how to take advantage of the Linux OS features that Docker uses to run programs securely, and how to manage shared resources. Using carefully-designed examples, the book teaches you how to orchestrate containers and applications from installation to removal. Along the way, you'll learn techniques for using Docker on systems ranging from your personal dev-and-test machine to full-scale cloud deployments.

    C. R. Licklider, whose visionary dream of a human-computer symbiosis transformed the course of modern science and led to the development of the personal computer. Reprint.

    With an emphasis on improvements and new features in Spark 2.0, authors Bill Chambers and Matei Zaharia break down Spark topics into distinct sections, each with unique goals. You’ll explore the basic operations and common functions of Spark’s structured APIs, as well as Structured Streaming, a new high-level API for building end-to-end streaming applications. Developers and system administrators will learn the fundamentals of monitoring, tuning, and debugging Spark, and explore machine learning techniques and scenarios for employing MLlib, Spark’s scalable machine-learning library. Get a gentle overview of big data and Spark Learn about DataFrames, SQL, and Datasets—Spark’s core APIs—through worked examples Dive into Spark’s low-level APIs, RDDs, and execution of SQL and DataFrames Understand how Spark runs on a cluster Debug, monitor, and tune Spark clusters and applications Learn the power of Structured Streaming, Spark’s stream-processing engine Learn how you can apply MLlib to a variety of problems, including classification or recommendation

    Python is considered easy to learn, but there's no quicker way to mastery of the language than learning from an expert teacher. This edition of "Learning Python" puts you in the hands of two expert teachers, Mark Lutz and David Ascher, whose friendly, well-structured prose has guided many a programmer to proficiency with the language. "Learning Python," Second Edition, offers programmers a comprehensive learning tool for Python and object-oriented programming. Thoroughly updated for the numerous language and class presentation changes that have taken place since the release of the first edition in 1999, this guide introduces the basic elements of the latest release of Python 2.3 and covers new features, such as list comprehensions, nested scopes, and iterators/generators. Beyond language features, this edition of "Learning Python" also includes new context for less-experienced programmers, including fresh overviews of object-oriented programming and dynamic typing, new discussions of program launch and configuration options, new coverage of documentation sources, and more. There are also new use cases throughout to make the application of language features more concrete. The first part of "Learning Python" gives programmers all the information they'll need to understand and construct programs in the Python language, including types, operators, statements, classes, functions, modules and exceptions. The authors then present more advanced material, showing how Python performs common tasks by offering real applications and the libraries available for those applications. Each chapter ends with a series of exercises that will test your Python skills and measure your understanding."Learning Python," Second Edition is a self-paced book that allows readers to focus on the core Python language in depth. As you work through the book, you'll gain a deep and complete understanding of the Python language that will help you to understand the larger application-level examples that you'll encounter on your own. If you're interested in learning Python--and want to do so quickly and efficiently--then "Learning Python," Second Edition is your best choice.

    But Neil Armstrong and Sally Ride would have never made history, and humankind would not have touched the stars, if not for the men and women on the ground who lit the fuse that launched the first rockets.Enthralled as a boy by the exploits of Flash Gordon and the novels of Robert Heinlein and Arthur C. Clarke—who put the science in science fiction—James French became one of the original unsung engineers of America’s groundbreaking space program. His fascinating memoir offers an up-close-and-technical look at building, testing, and perfecting the pioneering Saturn rockets and original lunar landing module, and he shares true tales, both humorous and harrowing, of life—and near death—on the front lines of scientific exploration.If you’ve ever said, “It’s not rocket science,” you’re right. It’s rocket engineering—and here’s your chance to marvel at how it changed the world and made it possible to explore all that lies beyond Earth. James R French graduated from MIT in 1958 with a degree of BSME Specializing in Propulsion. His first job was with Rocketdyne Division of North American Aviation where he worked on developmental testing of H-1 engines and combustion devices hardware for F-1 and J-2 engines used in Saturn 5. Mr. French has also worked at TRW Systems, where he was Lead Development Test Engineer on the Lunar Module Descent Engine, and Jet Propulsion Laboratory where he was Advanced Planetary studies Manager as well as Chief Engineer for the SP-100 Space Nuclear Power System and worked on Mariners 5, 6, 7, 8, and 9; Viking 1 & 2 and Voyager 1 & 2. . In 1986, he helped found American Rocket Co., a commercial launch company.Since 1987, Mr. French has been consultant to a variety of aerospace companies, SDIO, NASA, and USAF. He has participated in various startup companies in the private space flight arena and currently consults extensively to Blue Origin. Mr. French is co-author with Dr. Michael Griffin of the best-selling text Space Vehicle Design, published by AIAA. The second edition of the book has received the Summerfield Book Award for 2008. Mr. French is a Fellow of both AIAA and the British Interplanetary Society and a 50+ year member of AIAA. He has held several Technical Committee and other posts in AIAA. Cover design by Evan Twohy

    It is hyper-intelligent, aware, and evolving. Deever wants to use his creation for the good of all, to help fix a broken world, but knowing what a powerful weapon it could be in the wrong hands, he hides it. When his secret is uncovered, he is forced to plunge into a high-tech morass of deception and treachery to avoid catastrophe and save a world where humans are no longer the most intelligent species.

    This well-established two-book course is designed for class teaching and private study leading to GCSE examinations in mathematics and further Mathematics at A Level.

    Written for the engineer as well as the race car enthusiast, the authors, who developed many of the original vehicle dynamics theories and principles covered in this book, including the Moment Method, pair analysis and lap time simulation, include much information that is not available in any other vehicle dynamics text.

    Divided into two parts, the first part of the book introduces the programming language Prolog, while the second part teaches Artificial Intelligence using Prolog as a tool for the implementation of AI techniques. Prolog has its roots in logic, however the main aim of this book is to teach Prolog as a practical programming tool. This text therefore concentrates on the art of using the basic mechanisms of Prolog to solve interesting problems. The third edition has been fully revised and extended to provide an even greater range of applications, which further enhance its value as a self-contained guide to Prolog, AI or AI Programming for students and professional programmers alike.