Introduction to Deep Learning and Neural Networks with Python: A Comprehensive Guide

Introduction to Deep Learning and Neural Networks with Python: A Comprehensive Guide is an in-depth, step-by-step guide designed for neuroscientists to fully understand, practice, and build neural networks. This guide provides a practical approach to understanding the inner workings of neural networks, starting from the simplest model Y=X and building from scratch.

Understanding Neural Network Calculations

Providing math and Python code examples, this guide clarifies neural network calculations, enabling readers to fully understand how neural networks work by the end of the book. Detailed explanations are provided on how a generic gradient descent algorithm works, based on mathematical and Python examples, teaching readers how to use the gradient descent algorithm to manually perform all calculations in both the forward and backward passes of training a neural network.

A Problem-Based Approach to Building Artificial Neural Networks

This guide examines the practical side of deep learning and neural networks, providing a problem-based approach to building artificial neural networks using real data. It describes Python functions and features for neuroscientists, using a careful tutorial approach to describe the implementation of neural networks in Python.

Math and Code Examples

Math and code examples, available via a companion website, are included with helpful instructions for easy implementation. These examples provide a hands-on approach to understanding and applying neural network concepts.

Key Features

This comprehensive guide offers several key features, including:

• A practical and problem-based approach to building artificial neural networks using real data.
• Detailed explanations and examples of neural network calculations, including the gradient descent algorithm.
• A focus on Python functions and features relevant to neuroscientists.
• Math and code examples available via a companion website, with helpful instructions for implementation.

A Valuable Resource for Neuroscientists

Introduction to Deep Learning and Neural Networks with Python: A Comprehensive Guide is a valuable resource for neuroscientists looking to understand and apply deep learning and neural networks. With its practical approach, detailed explanations, and hands-on examples, this guide provides everything needed to start building and working with neural networks.