Learn IT - Books tags neural

TensorFlow for Deep Learning: From Linear Regression to Reinforcement Learning

O'Reilly, 2018

Learn how to solve challenging machine learning problems with TensorFlow, Google’s revolutionary new software library for deep learning. If you have some background in basic linear algebra and calculus, this practical book introduces machine-learning fundamentals by showing you how to design systems capable of detecting objects...

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...

Introduction to Machine Learning with R: Rigorous Mathematical Analysis

O'Reilly, 2018

Machine learning is an intimidating subject until you know the fundamentals. If you understand basic coding concepts, this introductory guide will help you gain a solid foundation in machine learning principles. Using the R programming language, you’ll first start to learn with regression modelling and then move into more...

Applied Text Analysis with Python: Enabling Language-Aware Data Products with Machine Learning

O'Reilly, 2018

From news and speeches to informal chatter on social media, natural language is one of the richest and most underutilized sources of data. Not only does it come in a constant stream, always changing and adapting in context; it also contains information that is not conveyed by traditional data sources. The key to unlocking natural...

Java: Data Science Made Easy

Packt Publishing, 2017

Data collection, processing, analysis, and more

About This Book

Your entry ticket to the world of data science with the stability and power of Java

Explore, analyse, and visualize your data effectively using easy-to-follow examples

A highly practical course covering a broad...

Neural Networks and Pattern Recognition

Academic Press, 1998

This book is one of the most up-to-date and cutting-edge texts available on the rapidly growing application area of neural networks. Neural Networks and Pattern Recognition focuses on the use of neural networksin pattern recognition, a very important application area for neural networks technology. The contributors are widely known...

Embedded Robotics: Mobile Robot Design and Applications with Embedded Systems

Springer, 2006

This book presents a unique combination of mobile robots and embedded systems, from introductory to intermediate level. It is structured in three parts, dealing with embedded systems (hardware and software design, actuators, sensors, PID control, multitasking), mobile robot design (driving, balancing, walking, and flying robots), and mobile...

Fundamentals of Deep Learning: Designing Next-Generation Machine Intelligence Algorithms

O'Reilly, 2017

With the reinvigoration of neural networks in the 2000s, deep learning has become an extremely active area of research, one that’s paving the way for modern machine learning. In this practical book, author Nikhil Buduma provides examples and clear explanations to guide you through major concepts of this complicated field.

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Machine Learning in Computer Vision (Computational Imaging and Vision)

Springer, 2005

The goal of computer vision research is to provide computers with humanlike
perception capabilities so that they can sense the environment, understand
the sensed data, take appropriate actions, and learn from this experience in
order to enhance future performance. The field has evolved from the application
of classical pattern...

R Deep Learning Essentials

Packt Publishing, 2016

Key Features

Harness the ability to build algorithms for unsupervised data using deep learning concepts with R

Master the common problems faced such as overfitting of data, anomalous datasets, image recognition, and performance tuning while building the models

Build models relating to neural...

Mobile Robots: The Evolutionary Approach (Studies in Computational Intelligence)

Springer, 2007

The design and control of autonomous intelligent mobile robotic systems operating in unstructured changing environments includes many objective difficulties. There are several studies about the ways in which, robots exhibiting some degree of autonomy, adapt themselves to fit in their environments. The application and use of bio-inspired and...

Adaptive Business Intelligence

Springer, 2006

From the reviews of the first edition:

"In this book the authors explain the science and application of numerous prediction and optimization techniques as well as how these concepts can be used to develop adaptive decision-making systems. The book is suitable for business and IT managers who make decisions in complex industrial and...

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