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Neural networks. Part 5, Introduction to real-world machine learning

Author: Alessandra Staglianò; Angie Ma; Gary Willis
Publisher: [Place of publication not identified] : O'Reilly, [2017]
Edition/Format:   eVideo : Clipart/images/graphics : English
Summary:
"Neural networks form the foundation for deep learning, the most advanced and popular machine learning technique in use today. This course provides an introduction to neural networks. It begins with an overview of a neural network's basic concepts and building blocks - neurons, weights, activations, and layers - before explaining how to train one using gradient descent. The optimization technique is explained with a  Read more...
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Details

Material Type: Clipart/images/graphics, Internet resource, Videorecording
Document Type: Internet Resource, Computer File, Visual material
All Authors / Contributors: Alessandra Staglianò; Angie Ma; Gary Willis
OCLC Number: 1004966453
Notes: Title from title screen (viewed September 28, 2017).
Date of publication taken from resource description page.
"Part 5 of 6."
Performer(s): Presenters, Alessandra Staglianò, Angie Ma, and Gary Willis.
Description: 1 online resource (1 streaming video file (43 min., 16 sec.)) : digital, sound, color
Other Titles: Introduction to real-world machine learning
Responsibility: with Alessandra Staglianò, Angie Ma, and Gary Willis.

Abstract:

"Neural networks form the foundation for deep learning, the most advanced and popular machine learning technique in use today. This course provides an introduction to neural networks. It begins with an overview of a neural network's basic concepts and building blocks - neurons, weights, activations, and layers - before explaining how to train one using gradient descent. The optimization technique is explained with a visual example and different issues such as parameter initialization and model validation are discussed. The course covers the different types of neural network architectures, explains the differences between them, and illustrates practical applications for each. Because training a neural network can be very slow, the course will offer up some tricks for speeding up the process and improving results. The course ends with a review of the history of this fascinating field, from its origin to its fall, and then its subsequent rise in modern days. Requirements include a clear understanding of supervised learning and optimization."--Resource description page.

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