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