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 created a face recognition system based on a Siamese neural network. The face recognition system uses a webcam to stream frames from a live video to a pre-trained Siamese neural network, and using a true image of the user, the system is able to authenticate the user in front of the webcam.

We first dissected the face recognition problem into smaller subproblems, and we saw how a face recognition system first performs a face detection step to isolate the face from the rest of the image, before the actual face recognition step. We saw how face detection is commonly done by the Viola-Jones algorithm, which uses Haar features to detect faces in real time. Face detection using Haar filters is implemented in Python via the OpenCV library, which allows us to perform face detection in just a few lines of code.

We then focused on face recognition, and we discussed...