This book looks at searchable storage in cloud computing. It introduces and presents new schemes for exploring and exploiting the searchable storage via cost-efficient semantic hashing computation. Authors Dr.Yu Hua and Dr. Xue (Steve) Liu cover topics from basic hashing structures (Bloom filters, locality sensitive hashing, cuckoo hashing), semantic storage systems, and searchable namespace, which support multiple applications, such as cloud backups, exact and approximate queries and image analytics.)

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This short book is ideal for front-end developers who need a rapid introduction to Vue.js. Author Nilson Jacques shows how the component-based architecture of Vue.js is designed to be flexible and easy to adopt, making it just as easy to integrate into projects and use alongside non-Vue code. If you've already used a component-based JavaScript framework such as React, you'll find this book an easy read, but it's also suitable for readers with no prior experience of such frameworks. 

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This book is designed to present fundamentals of data structures for beginners using the C++ programming language in a friendly, self-teaching, format. Author D. Malhotra integrates practical analogies and real world applications throughout the text to explain technical concepts. The book includes a variety of end-of-chapter practice exercises, including programming, theoretical, and multiple-choice tests..

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By using the PropEr framework in both Erlang and Elixir, this book teaches how to automatically generate test cases, test stateful programs, and change how you design your software for more principled and reliable approaches. Author Fred Hebert shows how you can better explore the problem space, validate the assumptions you make when coming up with program behavior, and expose unexpected weaknesses in your design. 

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This concise, project-driven guide to deep learning takes readers through a series of program-writing tasks that introduce them to the use of deep learning in such areas of artificial intelligence as computer vision, natural-language processing, and reinforcement learning. Author Eugene Charniak, a longtime artificial intelligence researcher specializing in natural-language processing, covers feed-forward neural nets, convolutional neural nets, word embeddings, recurrent neural nets, sequence-to-sequence learning, deep reinforcement learning, unsupervised models, and other fundamental concepts and techniques. The book illustrates the basics of deep learning by working through programs in the Tensorflow open-source machine learning framework

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This book starts with an introduction to PyTorch and tensors, the data structure used to calculate arithmetic operations. Author Pradeepta Mishra then looks at probability distributions using PyTorch, and dives into transformations and graph computations. Along the way the book covers common issues faced with neural network implementation and tensor differentiation. Moving on to algorithms it shows how PyTorch works with supervised and unsupervised algorithms, as well as how convolutional neural networks, deep neural networks, and recurrent neural networks work using it. The book concludes with natural language processing and text processing using PyTorch.

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This book looks at using the GeoServer open source, server-side software for sharing and editing geospatial data. Author Ben Mearns starts by showing how to develop a spatial analysis platform with web processing services, before moving on to more advanced topics such as tile caching. The book also shows how to produce a performant GeoServer-backed web mapping application, and how to enable authentication on the frontend and backend to protect sensitive map data, and deliver sensitive data to end users.

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This book covers the basic game development process using Python, covering game topics including graphics, sound, artificial intelligence, animation, game engines, etc. Author James R. Parker creates real games as you work through the text and significant parts of a game engine are built and made available for download. The companion disc contains all of the resources described in the book, e.g. example code, game assets, video/sound editing software, and color figures.

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This book aims to show that Julia is an accessible, intuitive, and highly efficient base language with speed that exceeds R and Python. Authors Paul D. McNicholas and Peter Tait get readers up to speed on key features of the Julia language and illustrate its facilities for data science and machine learning work. Using well known data science methods, the book shows what makes Julia a formidable language for data science.

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This is an intermediate-level tutorial that introduces Angular and TypeScript to developers comfortable with building web applications using other frameworks and tools. Authors Yakov Fain and Anton Moiseev cover core concerns like state management, data, forms, and server communication as you build a full-featured online auction app.  They also show how to write type-aware classes, interfaces, and generics with TypeScript, along with time-saving best practices to use in your own work.

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This book is designed as an advanced manual for C++ programming that aims to help experienced developers get more out of the latest release and this 4th edition has been revised to cover the changes to C++17. Author Marc Gregoire skims over the basics and dives right in to exploiting the full capabilities of C++17. Each feature is explained by example, each including actual code snippets that you can plug into your own applications. Case studies include extensive, working code that has been tested on Windows and Linux, and the book has expert tips, tricks, and workarounds.

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The second edition of this hands-on book, with the subtitle, Mastering Data-Driven Finance, has been updated to Python 3. Author Dr. Yves Hilpisch helps you get started with the language, guiding developers and quantitative analysts through Python libraries and tools for building financial applications and interactive financial analytics. Using practical examples throughout the book, Hilpisch also shows you how to develop a full-fledged framework for Monte Carlo simulation-based derivatives and risk analytics, based on a large, realistic case study. Much of the book uses interactive IPython Notebooks.

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