Learn IT - Books tags vector

Machine Learning with Python Cookbook: Practical Solutions from Preprocessing to Deep Learning

O'Reilly, 2018

This practical guide provides nearly 200 self-contained recipes to help you solve machine learning challenges you may encounter in your daily work. If you’re comfortable with Python and its libraries, including pandas and scikit-learn, you’ll be able to address specific problems such as loading data, handling text or...

Combinatorial Methods with Computer Applications (Discrete Mathematics and Its Applications)

CRC Press, 2007

Combinatorial Methods with Computer Applications provides in-depth coverage of recurrences, generating functions, partitions, and permutations, along with some of the most interesting graph and network topics, design constructions, and finite geometries. Requiring only a foundation in discrete mathematics, it can serve as the...

Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems

O'Reilly, 2017

Graphics in this book are printed in black and white.

Through a series of recent breakthroughs, deep learning has boosted the entire field of machine learning. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data....

The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition (Springer Series in Statistics)

Springer, 2019

This book describes the important ideas in a variety of fields such as medicine, biology, finance, and marketing in a common conceptual framework. While the approach is statistical, the emphasis is on concepts rather than mathematics. Many examples are given, with a liberal use of colour graphics. It is a valuable resource...

Data Mining: Concepts, Models, Methods, and Algorithms, Second Edition

John Wiley & Sons, 2011

Now updated—the systematic introductory guide to modern analysis of large data sets

As data sets continue to grow in size and complexity, there has been an inevitable move towards indirect, automatic, and intelligent data analysis in which the analyst works via more complex and sophisticated software tools. This book...

Data Mining and Statistics for Decision Making

John Wiley & Sons, 2011

Data mining is the process of automatically searching large volumes of data for models and patterns using computational techniques from statistics, machine learning and information theory; it is the ideal tool for such an extraction of knowledge. Data mining is usually associated with a business or an organization's need to identify...

C++ Toolkit for Engineers and Scientists

Springer, 1999

This concise guide covers the fundamental aspects of the numerical analysis, basing upon it the construction of its routines for solving nonlinear equations, linear and nonlinear systems of equations, and eigenvalue problems. Focusing on software development, this book emphasizes software tools, OOP techniques for handling vectors, polynomials,...

Sams Teach Yourself JavaScript in 21 Days

Sams Publishing, 2002

The aim of Sams Teach Yourself JavaScript in 21 Days is to tutor the Web-literate novice JavaScripter through to a high level of competency in applying JavaScript to Web pages.

In addition to core uses of client-side JavaScript, Sams Teach Yourself JavaScript in 21 Days also covers emerging topics such as...

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...

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...

Computational Optimization, Methods and Algorithms (Studies in Computational Intelligence)

Springer, 2011

Computational optimization is an important paradigm with a wide range of applications. In virtually all branches of engineering and industry, we almost always try to optimize something - whether to minimize the cost and energy consumption, or to maximize profits, outputs, performance and efficiency. In many cases, this search for...

Graph-Based Clustering and Data Visualization Algorithms (SpringerBriefs in Computer Science)

Springer, 2013

This work presents a data visualization technique that combines graph-based topology representation and dimensionality reduction methods to visualize the intrinsic data structure in a low-dimensional vector space. The application of graphs in clustering and visualization has several advantages. A graph of important edges (where edges...

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