Learn IT - Books tags numeric

Kernel Based Algorithms for Mining Huge Data Sets: Supervised, Semi-supervised, and Unsupervised Learning

Springer, 2006

"Kernel Based Algorithms for Mining Huge Data Sets" is the first book treating the fields of supervised, semi-supervised and unsupervised machine learning collectively. The book presents both the theory and the algorithms for mining huge data sets by using support vector machines (SVMs) in an iterative way. It demonstrates how kernel...

Data Mining In Time Series Databases (Series in Machine Perception and Artificial Intelligence)

World Scientific Publishing, 2004

Traditional data mining methods are designed to deal with “static” databases, i.e. databases where the ordering of records (or other database objects) has nothing to do with the patterns of interest. Though the assumption of order irrelevance may be sufficiently accurate in some applications, there are certainly many other cases, where...

Oracle CRM On Demand Reporting (Osborne Oracle Press)

McGraw-Hill, 2008

Your One-Stop Guide to Oracle CRM On Demand Reporting

Plan, build, and distribute data-rich business intelligence reports with ease. Oracle CRM On Demand Reporting details the entire report-building process and explains how to use the powerful features available in Answers On Demand to create and share...

Extreme NXT: Extending the LEGO MINDSTORMS NXT to the Next Level, Second Edition

Apress, 2009

Although LEGO MINDSTORMS NXT allows anyone to build complex inventions, there are limits to what you can do with what comes inside the box. This book shows you how to advance the NXT with more than 45 exciting projects that include creating a cool magic wand that writes words in thin air, building a remotely guided vehicle, and constructing...

Feature Engineering for Machine Learning: Principles and Techniques for Data Scientists

O'Reilly, 2018

Feature engineering is a crucial step in the machine-learning pipeline, yet this topic is rarely examined on its own. With this practical book, you’ll learn techniques for extracting and transforming features—the numeric representations of raw data—into formats for machine-learning models. Each chapter guides you...

How to Pass Data Interpretation Tests: Unbeatable Practice for Numerical and Quantitative Reasoning and Problem Solving Tests (Testing Series)

Kogan Page, 2009

This book provides everything you need for a successful programme of revision or review. It contains 330 practice questions with answers and explanations, advice on how to show your true potential and signposts to sources of further practice material.

Tests of data interpretation are fast...

Interpreting and Visualizing Regression Models Using Stata

Stata Press, 2020

Interpreting and Visualizing Regression Models Using Stata, Second Edition provides clear and simple examples illustrating how to interpret and visualize a wide variety of regression models. Including over 200 figures, the book illustrates linear models with continuous predictors (modeled linearly, using polynomials,...

Text Mining: Predictive Methods for Analyzing Unstructured Information

Springer, 2004

The growth of the web can be seen as an expanding public digital library collection. Online digital information extends far beyond the web and its publicly available information. Huge amounts of information are private and are of interest to local communities, such as the records of customers of a business. This information is overwhelmingly...

Derivatives Analytics with Python: Data Analysis, Models, Simulation, Calibration and Hedging (The Wiley Finance Series)

John Wiley & Sons, 2015

Supercharge options analytics and hedging using the power ofPython

Derivatives Analytics with Python shows you how toimplement market-consistent valuation and hedging approaches usingadvanced financial models, efficient numerical techniques, and thepowerful capabilities of the Python programming language. Thisunique...

MATLAB Symbolic Algebra and Calculus Tools

Apress, 2014

MATLAB is a high-level language and environment for numerical computation, visualization, and programming. Using MATLAB, you can analyze data, develop algorithms, and create models and applications. The language, tools, and built-in math functions enable you to explore multiple approaches and reach a solution faster than with spreadsheets or...

Perl Medic : Transforming Legacy Code

Addison Wesley, 2004

Bring new power, performance, and scalability to your existing Perl code!

Today's Perl developers spend 60-80% of their time working with existing Perl code. Now, there's a start-to-finish guide to understanding that code, maintaining it, updating it, and refactoring it for maximum performance and reliability. Peter J. Scott, lead author of...

Hybrid Imaging and Visualization: Employing Machine Learning with Mathematica - Python

Springer, 2019

The book introduces the latest methods and algorithms developed in machine and deep learning (hybrid symbolic-numeric computations, robust statistical techniques for clustering and eliminating data as well as convolutional neural networks) dealing not only with images and the use of computers, but also their applications to visualization tasks...

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