Introduction to Deep Learning and Neural Networks with PythonT

Introduction to Deep Learning and Neural Networks with PythonT: A Practical Guide is an intensive step-by-step guide for neuroscientists to fully understand, practice, and build neural networks. Providing math and PythonT 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 PythonT 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 PythonT functions and features for neuroscientists
• Uses a careful tutorial approach to describe implementation of neural networks in PythonT
• Features math and code examples (via companion website) with helpful instructions for easy implementation