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

A new reference on how to build an artificial neural network in Python™, with examples

Ahmed Fawzy Gad (Author), Fatima Ezzahra Jarmouni (Author)

9780323909334, Elsevier Science

Paperback / softback, published 26 November 2020

300 pages
22.9 x 15.1 x 2 cm, 0.48 kg

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.

1. Preparing the Development Environment2. Introduction to ANN3. ANN with 1 Input and 1 Output4. Working with Any Number of Inputs5. Working with Hidden Layers6. Using Any Number of Hidden Neurons7. ANN with 2 Hidden Layers8. ANN with 3 Hidden Layers9. Any Number of Hidden Layers10. Generic ANN11. Speeding Neural Network using Cython and PyPy12. Deploying Neural Network to Mobile Devices

Subject Areas: Neurosciences [PSAN], Science: general issues [PD], Physiology [MFG]