Even experienced JavaScripters sometimes find themselves overwhelmed as complex apps grow into a tangled web of callbacks.With Async JavaScript, you'll learn about:Event schedulingThe PubSub patternPromises and Deferred objectsFlow control with Async.jsRecipes for common async scenariosMulti-threading with Web WorkersAltJS languagesand more, with examples tailored to jQuery and Node.js.

    With this concise book, you will quickly gain working knowledge of Sinatra and its minimalist approach to building both standalone and modular web applications. Sinatra serves as a lightweight wrapper around Rack middleware, with syntax that maps closely to functions exposed by HTTP verbs, which makes it ideal for web services and APIs. If you have experience building applications with Ruby, you’ll quickly learn language fundamentals and see under-the-hood techniques, with the help of several practical examples. Then you’ll get hands-on experience with Sinatra by building your own blog engine. Learn Sinatra’s core concepts, and get started by building a simple application Create views, manage sessions, and work with Sinatra route definitions Become familiar with the language’s internals, and take a closer look at Rack Use different subclass methods for building flexible and robust architectures Put Sinatra to work: build a blog that takes advantage of service hooks provided by the GitHub API

    Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. This book introduces a broad range of topics in deep learning.The text offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning. It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology; and it surveys such applications as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames. Finally, the book offers research perspectives, covering such theoretical topics as linear factor models, autoencoders, representation learning, structured probabilistic models, Monte Carlo methods, the partition function, approximate inference, and deep generative models.Deep Learning can be used by undergraduate or graduate students planning careers in either industry or research, and by software engineers who want to begin using deep learning in their products or platforms. A website offers supplementary material for both readers and instructors.

    Readers will progress from building a basic physics engine to creating intelligent moving objects and complex systems, setting the foundation for further experiments in generative design. Subjects covered include forces, trigonometry, fractals, cellular automata, self-organization, and genetic algorithms. The book's examples are written in Processing, an open-source language and development environment built on top of the Java programming language. On the book's website (http://www.natureofcode.com), the examples run in the browser via Processing's JavaScript mode.