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 autoencoders

Another interesting application of autoencoders is image denoising. Image noise is defined as a random variations of brightness in an image. Image noise may originate from the sensors of digital cameras. Although digital cameras these days are capable of capturing high quality images, image noise may still occur, especially in low light conditions.

Denoising images has been a challenge for researchers for many years. Early methods include applying some sort of image filter (that is, mean averaging filter, where the pixel value is replaced with the average pixel value of its neighbors) over the image. However, such methods can sometimes fall short and the effects can be less than ideal.

A few years ago, researchers discovered that we can train autoencoders for image denoising. The idea is simple. Instead of using the same input and output when training conventional...