Introduction to Deep Learning and Neural Networks with Python (TM): A Practical Guide, Ahmed Fawzy Gad (Researcher and Assistant Lecturer, Menoufia University, Egypt),Fatima Ezzahra Jarmouni (Ecole Nationale Superieure d'Informatique et d'Analyse des Systemes, Rabat, Morocco) (9780323909334) — Readings Books

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Introduction to Deep Learning and Neural Networks with Python (TM): A Practical Guide
Paperback

Introduction to Deep Learning and Neural Networks with Python ™: A Practical Guide

$292.95
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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.

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Format
Paperback
Publisher
Elsevier Science & Technology
Country
United Kingdom
Date
26 November 2020
Pages
300
ISBN
9780323909334

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.

Read More
Format
Paperback
Publisher
Elsevier Science & Technology
Country
United Kingdom
Date
26 November 2020
Pages
300
ISBN
9780323909334