Book Image

Neural Network Projects with Python

By : James Loy
Book Image

Neural Network Projects with Python

By: James Loy

Overview of this book

Neural networks are at the core of recent AI advances, providing some of the best resolutions to many real-world problems, including image recognition, medical diagnosis, text analysis, and more. This book goes through some basic neural network and deep learning concepts, as well as some popular libraries in Python for implementing them. It contains practical demonstrations of neural networks in domains such as fare prediction, image classification, sentiment analysis, and more. In each case, the book provides a problem statement, the specific neural network architecture required to tackle that problem, the reasoning behind the algorithm used, and the associated Python code to implement the solution from scratch. In the process, you will gain hands-on experience with using popular Python libraries such as Keras to build and train your own neural networks from scratch. By the end of this book, you will have mastered the different neural network architectures and created cutting-edge AI projects in Python that will immediately strengthen your machine learning portfolio.
Table of Contents (10 chapters)

Summary

In this chapter, we have seen what machine learning is, and looked at the complete end-to-end workflow for every machine learning project. We have also seen what neural networks and deep learning is, and coded up our own neural network from scratch and in Keras.

For the rest of the book, we will create our own real-world neural network projects. Each chapter will cover one project, and the projects are listed in order of increasing complexity. By the end of the book, you will have created your own neural network projects in medical diagnosis, taxi fare predictions, image classification, sentiment analysis, and much more. In the next chapter, Chapter 2, Predicting Diabetes with Multilayer Perceptrons we will cover diabetes prediction with multilayer perceptrons (MLPs). Let's get started!