Introduction to Deep Learning and Neural Networks with Python™

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

Neuroscientists, especially those who work in systems and computational neuroscience who want to build artificial neural networks. Additional researchers who use Python™ or want to learn how. Researchers in biomedical engineering and neural engineering

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