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)

One-shot learning

Given the unique requirements and constraints faced by facial recognition systems, it is clear that the paradigm of training a CNN for classification using a huge dataset (known as batch learning classification) is unsuitable for the facial recognition problem. Instead, our objective is to create a neural network that can learn to recognize any face using just a single training sample. This form of neural network training is known as one-shot learning.

One-shot learning brings about a new and interesting paradigm in machine learning problems. Thus far, we have thought of machine learning problems as mostly classification problems. In Chapter 2, Predicting Diabetes, with Multilayer Perceptrons, we used an MLP to classify patients at risk of diabetes. In Chapter 4, Cats Versus Dogs – Image Classification Using CNNs, we used a CNN to classify images of cats...