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)

Denoising documents with autoencoders

So far, we have applied our denoising autoencoder on the MNIST dataset, which is a pretty simple dataset. Let's take a look now at a more complicated dataset, which better represents the challenges of denoising documents in real life.

The dataset that we will be using is provided for free by the University of California Irvine (UCI). For more information on the dataset, you can visit UCI's website at https://archive.ics.uci.edu/ml/datasets/NoisyOffice.

The dataset can be found in the accompanying GitHub repository for this book. For more information on downloading the code and dataset for this chapter from the GitHub repository, please refer to the Technical requirements section earlier in the chapter.

The dataset consists of 216 different noisy images. The noisy images are scanned office documents that are tainted by coffee stains...