Introduction to Deep Learning and Neural Networks with Python™

A Practical Guide

Ahmed Fawzy Gad, Fatima Ezzahra Jarmouni

• Éditeur: Academic Press
• Année: 2020
• ISBN:
  9780323909341
• Sujet: Ciencias biológicas
• Sous-sujet: Neurociencia

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