Learn IT - Books tags brief

Digital Signal Processing with Examples in MATLAB® (Electrical Engineering & Applied Signal Processing Series)

CRC Press, 2011

Based on fundamental principles from mathematics, linear systems, and signal analysis, digital signal processing (DSP) algorithms are useful for extracting information from signals collected all around us. Combined with today’s powerful computing capabilities, they can be used in a wide range of application areas, including...

Numerical Methods for Chemical Engineers with MATLAB Applications

Prentice Hall, 1999

Master numerical methods using MATLAB, today's leading software for problem solving.

This complete guide to numerical methods in chemical engineering is the first to take full advantage of MATLAB's powerful calculation environment....

Introductory Statistics with R (Statistics and Computing)

Springer, 2008

This book provides an elementary-level introduction to R, targeting both non-statistician scientists in various fields and students of statistics. The main mode of presentation is via code examples with liberal commenting of the code and the output, from the computational as well as the statistical viewpoint. Brief sections introduce the...

Digital Geometry Algorithms: Theoretical Foundations and Applications to Computational Imaging (Lecture Notes in Computational Vision and Biomechanics)

Springer, 2012

Digital geometry emerged as an independent discipline in the second half of the last century. It deals with geometric properties of digital objects and is developed with the unambiguous goal to provide rigorous theoretical foundations for devising new advanced approaches and algorithms for various problems of visual computing. Different aspects...

Git in Practice

Manning Publications, 2014

Summary

Git in Practice is a collection of 66 tested techniques that will optimize the way you and your team manage your development projects. The book begins with a brief reminder of the core version control concepts you need when using Git and moves on to the high-value features you may not have explored...

Introduction to Scientific Computing: A Matrix Vector Approach Using MATLAB

Prentice Hall, 1996

This book presents a unique approach for one semester numerical methods and numerical analysis courses. Well organized but flexible, the text is brief and clear enough for introductory numerical analysis students to "get their feet wet," yet comprehensive enough in its treatment of problems and applications for higher-level students to...

C++ in a Nutshell

O'Reilly, 2003

C++ in a Nutshell packs an enormous amount of information on C++ (and the many libraries used with it) in an indispensable quick reference for those who live in a deadline-driven world and need the facts but not the frills. Cross-references link related methods, classes, and other key features. This is an...

Angular Test-Driven Development - Second Edition

Packt Publishing, 2017

Enhance your testing skills to build powerful and fault-free applications in Angular v4

About This Book

Learn test-driven development (TDD) for JavaScript

Improve your Karma and Protractor expertise by setting up an Angular test suite

A wide range of testing techniques for...

Deep Learning with Python: A Hands-on Introduction

Apress, 2017

Discover the practical aspects of implementing deep-learning solutions using the rich Python ecosystem. This book bridges the gap between the academic state-of-the-art and the industry state-of-the-practice by introducing you to deep learning frameworks such as Keras, Theano, and Caffe. The practicalities of these frameworks is often...

Functional Analysis Methods for Reliability Models (Pseudo-Differential Operators)

Birkhauser, 2011

People are often concerned with the reliability of products they use and of the friends and associates with whom they interact daily in a myriad of ways. Although the term “reliability” may not occur in the description of a given product or interaction, it is often the attribute that the individual means in expression of...

Approximation Methods for Polynomial Optimization: Models, Algorithms, and Applications (SpringerBriefs in Optimization)

Springer, 2012

Polynomial optimization, as its name suggests, is used to optimize a generic multivariate polynomial function, subject to some suitable polynomial equality and/or inequality constraints. Such problem formulation dates back to the nineteenth century when the relationship between nonnegative polynomials and sum of squares (SOS) was...

Learning scikit-learn: Machine Learning in Python

Packt Publishing, 2013

Incorporating machine learning in your applications is becoming essential. As a programmer this book is the ideal introduction to scikit-learn for your Python environment, taking your skills to a whole new level.

Overview

Use Python and scikit-learn to create intelligent applications

Apply...

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