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Python Deep Learning Projects: 9 projects demystifying neural network and deep learning models for building intelligent systems
Python Deep Learning Projects: 9 projects demystifying neural network and deep learning models for building intelligent systems

Insightful projects to master deep learning and neural network architectures using Python and Keras

Key Features

  • Explore deep learning across computer vision, natural language processing (NLP), and image processing
  • Discover best practices for the training of deep neural...
Introduction to Linear Algebra, Third Edition
Introduction to Linear Algebra, Third Edition

This informally written text provides students with a clear introduction into the subject of linear algebra. Topics covered include matrix multiplication, row reduction, matrix inverse, orthogonality and computation. The self-teaching book is loaded with examples and graphics and provides a wide array of probing problems, accompanying...

An Introduction to Statistical Learning: with Applications in R (Springer Texts in Statistics)
An Introduction to Statistical Learning: with Applications in R (Springer Texts in Statistics)

An Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance to marketing to astrophysics in the past twenty years. This book presents some of the most...

Mathematics for 3D Game Programming and Computer Graphics, Third Edition
Mathematics for 3D Game Programming and Computer Graphics, Third Edition

This updated third edition illustrates the mathematical concepts that a game developer needs to develop 3D computer graphics and game engines at the professional level. It starts at a fairly basic level in areas such as vector geometry and linear algebra, and then progresses to more advanced topics in 3D programming such as illumination and...

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

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...

Machine Learning Algorithms: Popular algorithms for data science and machine learning, 2nd Edition
Machine Learning Algorithms: Popular algorithms for data science and machine learning, 2nd Edition

An easy-to-follow, step-by-step guide for getting to grips with the real-world application of machine learning algorithms

Key Features

  • Explore statistics and complex mathematics for data-intensive applications
  • Discover new developments in EM algorithm, PCA, and bayesian...
Computer Processing of Remotely-Sensed Images: An Introduction
Computer Processing of Remotely-Sensed Images: An Introduction

Environmental remote sensing is the measurement, from a distance, of the spectral features of the Earth’s surface and atmosphere. These measurements are normally made by instruments carried by satellites or aircraft, and are used to infer the nature and characteristics of the land or sea surface, or of the atmosphere, at the...

Large-Scale Kernel Machines (Neural Information Processing series)
Large-Scale Kernel Machines (Neural Information Processing series)

Pervasive and networked computers have dramatically reduced the cost of collecting and distributing large datasets. In this context, machine learning algorithms that scale poorly could simply become irrelevant. We need learning algorithms that scale linearly with the volume of the data while maintaining enough statistical efficiency to...

Google Maps JavaScript API Cookbook
Google Maps JavaScript API Cookbook

This book will help you use the amazing resource that is Google Maps to your own ends. From showing maps on mobiles to creating GIS applications, this lively, recipe-packed guide is all you need.

Overview

  • Add to your website's functionality by utilizing Google Maps' power
  • Full of...
Semi-Supervised Learning (Adaptive Computation and Machine Learning)
Semi-Supervised Learning (Adaptive Computation and Machine Learning)
In the field of machine learning, semi-supervised learning (SSL) occupies the middle ground, between supervised learning (in which all training examples are labeled) and unsupervised learning (in which no label data are given). Interest in SSL has increased in recent years, particularly because of application domains in which unlabeled data are...
Getting Up To Speed: The Future Of Supercomputing
Getting Up To Speed: The Future Of Supercomputing
High-performance computing is important in solving complex problems in areas from climate and biology to national security. Several factors have led to the recent reexamination of the rationale for federal investment in research and development in support of high-performance computing, including continuing changes in the various component...
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Gaussian processes (GPs) provide a principled, practical, probabilistic approach to learning in kernel machines. GPs have received increased attention in the machine-learning community over the past decade, and this book provides a long-needed systematic and unified treatment of theoretical and practical aspects of GPs in machine learning. The...
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