Main description:

Introduction to Deep Learning and Neural Networks with Python (TM): A Practical Guide is an intensive step-by-step guide for neuroscientists to fully understand, practice, and build neural networks. Providing math and Python (TM) 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 (TM) 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.

Contents:

1. Preparing the Development Environment
2. Introduction to ANN
3. ANN with 1 Input and 1 Output
4. Working with Any Number of Inputs
5. Working with Hidden Layers
6. Using Any Number of Hidden Neurons
7. ANN with 2 Hidden Layers
8. ANN with 3 Hidden Layers
9. Any Number of Hidden Layers
10. Generic ANN
11. Speeding Neural Network using Cython and PyPy
12. Deploying Neural Network to Mobile Devices