Learning Latex

Here is a short, well-written book that covers the material essential for learning LaTeX. This manual includes the following crucial features: - numerous examples of widely used mathematical expressions; - complete documents illustrating the creation of articles, reports, presentations, and posters; - troubleshooting tips to help you pinpoint an error; - details of how to set up an index and a bibliography; and - information about online LaTeX resources. This second edition of the well-regarded and highly successful book includes additional material on - the American Mathematical Society packages for typesetting additional mathematical symbols and multi-line displays; - the BiBTeX program for creating bibliographies; - the Beamer package for creating presentations; and - the a0poster class for creating posters.

Create high-quality and professional-looking texts, articles, and books for Business and Science using LaTeX.

Published Nov 25, 2003 by Addison-Wesley Professional. Part of the Tools and Techniques for Computer Typesetting series. The series editor may be contacted at [email protected]. LaTeX is the text-preparation system of choice for scientists and academics, and is especially useful for typesetting technical materials. This popular book shows you how to begin using LaTeX to create high-quality documents. The book also serves as a handy reference for all LaTeX users. In this completely revised edition, the authors cover the LaTeX2ε standard and offer more details, examples, exercises, tips, and tricks. They go beyond the core installation to describe the key contributed packages that have become essential to LaTeX processing. Inside, you will find: Complete coverage of LaTeX fundamentals, including how to input text, symbols, and mathematics; how to produce lists and tables; how to include graphics and color; and how to organize and customize documents Discussion of more advanced concepts such as bibliographical databases and BIBTeX, math extensions with AMS-LaTeX, drawing, slides, and letters Helpful appendices on installation, error messages, creating packages, using LaTeX with HTML and XML, and fonts An extensive alphabetized listing of commands and their uses New to this edition: More emphasis on LaTeX as a markup language that separates content and form--consistent with the essence of XML Detailed discussions of contributed packages alongside relevant standard topics In-depth information on PDF output, including extensive coverage of how to use the hyperref package to create links, bookmarks, and active buttons As did the three best-selling editions that preceded it, Guide to LaTeX, Fourth Edition, will prove indispensable to anyone wishing to gain the benefits of LaTeX. The accompanying CD-ROM is part of the TeX Live set distributed by TeX Users Groups, containing a full LaTeX installation for Windows, MacOSX, and Linux, as well as many extensions, including those discussed in the book. 0321173856B10162003

This is the fourth edition of the standard introductory text and complete reference for scientists in all disciplines, as well as engineers. This fully revised version includes important updates on articles and books as well as information on a crucial new topic: how to create transparencies and computer projections, both for classrooms and professional meetings. The text maintains its user-friendly, example-based, visual approach, gently easing readers into the secrets of Latex with The Short Course. Then it introduces basic ideas through sample articles and documents. It includes a visual guide and detailed exposition of multiline math formulas, and even provides instructions on preparing books for publishers.

This book presents direct and concise explanations and examples to many LaTeX syntax and structures, allowing students and researchers to quickly understand the basics that are required for writing and preparing book manuscripts, journal articles, reports, presentation slides and academic theses and dissertations for publication. Unlike much of the literature currently available on LaTeX, which takes a more technical stance, focusing on the details of the software itself, this book presents a user-focused guide that is concerned with its application to everyday tasks and scenarios. It is packed with exercises and looks at topics like formatting text, drawing and inserting tables and figures, bibliographies and indexes, equations, slides, and provides valuable explanations to error and warning messages so you can get work done with the least time and effort needed. This means LaTeX in 24 Hours can be used by students and researchers with little or no previous experience with LaTeX to gain quick and noticeable results, as well as being used as a quick reference guide for those more experienced who want to refresh their knowledge on the subject.

An introduction to a broad range of topics in deep learning, covering mathematical and conceptual background, deep learning techniques used in industry, and research perspectives. “Written by three experts in the field, Deep Learning is the only comprehensive book on the subject.” —Elon Musk, cochair of OpenAI; cofounder and CEO of Tesla and SpaceX Deep learning is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts. 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.