With the resurgence of neural networks in the 2010s, deep learning has
become essential for machine learning practitioners and even many
software engineers. This book provides a comprehensive introduction for
data scientists and software engineers with machine learning experience.
You'll start with deep learning basics and move quickly to the details
of important advanced architectures, implementing everything from
scratch along the way.
Author Seth Weidman shows you how neural networks work using a first
principles approach. You'll learn how to apply multilayer neural
networks, convolutional neural networks, and recurrent neural networks
from the ground up. With a thorough understanding of how neural networks
work mathematically, computationally, and conceptually, you'll be set up
for success on all future deep learning projects.
This book provides:
- Extremely clear and thorough mental models--accompanied by working
code examples and mathematical explanations--for understanding neural
networks
- Methods for implementing multilayer neural networks from scratch,
using an easy-to-understand object-oriented framework
- Working implementations and clear-cut explanations of convolutional
and recurrent neural networks
- Implementation of these neural network concepts using the popular
PyTorch framework
