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)

Managing image data for Keras

One common problem encountered in neural network projects for image classification is that most computers do not have sufficient RAM to load the entire set of data into memory. Even for relatively modern and powerful computers, it would be far too slow to load the entire set of images into memory and to train a CNN from there.

To alleviate this problem, Keras provides a useful flow_from_directory method that takes as an input the path to the images, and generates batches of data as output. The batches of data are loaded into memory, as required before model training. This way, we can train a deep neural network on a huge number of images without worrying about memory issues. Furthermore, the flow_from_directory method allows us to perform image preprocessing steps such as resizing and other image augmentation techniques by simply passing an argument...