Introduction to Deep Learning and Neural Networks with Python™: A Practical Guide is an intensive step-by-step guide for neuroscientists to fully understand, practice, and build neural networks. Providing math and Python™ code examples to clarify neural network calculations, by book’s end readers will fully understand how neural networks work starting from the simplest model Y=X and building from scratch. Details and explanations are provided on how a generic gradient descent algorithm works based on mathematical and Python™ examples, teaching you how to use the gradient descent algorithm to manually perform all calculations in both the forward and backward passes of training a neural network. Examines the practical side of deep learning and neural networks Provides a problem-based approach to building artificial neural networks using real data Describes Python™ functions and features for neuroscientists Uses a careful tutorial approach to describe implementation of neural networks in Python™ Features math and code examples (via companion website) with helpful instructions for easy implementation
Introduction To Deep Learning And Neural Networks With Python™: A Practical Guide ,2020 Original PDF
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- Publisher: Elsevier Science; November 25, 2020
- Language: English
- ISBN: 9780323909334
- ISBN: 9780323909341
$27.00
Description
Additional information
Publisher |
Elsevier Science |
---|---|
Language |
English |
Format |
|
ISBN |
9780323909334 |
edition |
2020 |
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