Kategoria BISAC: Computers >> Data Science - Neural Networks

Artificial Intelligence is the most exciting technology of the century, and Deep Learning is, quite literally, the "brain" behind the world's smartest Artificial Intelligence systems out there.

Grokking Deep Learning is the perfect place to begin the deep learning journey. Rather than just learning the "black box" API of some library or framework, readers will actually understand how to build these algorithms completely from scratch.

Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.

Path Planning for Vehicles Operating in Uncertain 2D-environments presents a survey that includes several path planning methods developed using fuzzy logic, grapho-analytical search, neural networks, and neural-like structures, procedures of genetic search, and unstable motion modes.

• Presents a survey of accounting limitations imposed by vehicle dynamics
• Proposes modified and new original methods, including neural networking, grapho-analytical, and nature-inspired
• Gives tools for a novice researcher to select a method that would suit their needs or...

Roughly inspired by the human brain, deep neural networks trained with large amounts of data can solve complex tasks with unprecedented accuracy. This practical book provides an end-to-end guide to TensorFlow, the leading open source software library that helps you build and train neural networks for computer vision, natural language processing (NLP), speech recognition, and general predictive analytics.

Authors Tom Hope, Yehezkel Resheff, and Itay Lieder provide a hands-on approach to TensorFlow fundamentals for a broad technical audience--from data scientists and engineers to...

Reissue of the 1988 Expanded Edition with a new foreword by Leon Bottou

In 1969, ten years after the discovery of the perceptron -- which showed that a machine could be taught to perform certain tasks using examples -- Marvin Minsky and Seymour Papert published Perceptrons, their analysis of the computational capabilities of perceptrons for specific tasks. As Leon Bottou writes in his foreword to this edition, "Their rigorous work and brilliant technique does not make the perceptron look very good." Perhaps as a result, research turned away from the perceptron. Then the...

This must-read text/reference introduces the fundamental concepts of convolutional neural networks (ConvNets), offering practical guidance on using libraries to implement ConvNets in applications of traffic sign detection and classification. The work presents techniques for optimizing the computational efficiency of ConvNets, as well as visualization techniques to better understand the underlying processes. The proposed models are also thoroughly evaluated from different perspectives, using exploratory and quantitative analysis.

Learn how to solve challenging machine learning problems with Tensorflow, Google's revolutionary new system for deep learning. If you have some background with basic linear algebra and calculus, this practical book shows you how to build--and when to use--deep learning architectures. You'll learn how to design systems capable of detecting objects in images, understanding human speech, analyzing video, and predicting the properties of potential medicines.

TensorFlow for Deep Learning teaches concepts through practical examples and builds understanding of deep learning...

The phrase "Machine Learning" refers to the automatic detection of meaningful data by computing systems. In the last few decades, it has become a common tool in almost any task that needs to understand data from large data sets. One of the biggest application of machine learning technology is the search engine. Search engines learn how to provide the best results based on historic, trending, and relative data sets. When you look at anti-spam software, it learns how to filter email messages....