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)

A review of modern CNNs

Now that we've seen the basic architecture of CNNs, let's take a look at modern, state-of-the-art CNNs. We'll do a walk-through of the evolution of CNNs, and see how they have changed over the years. We'll not go into the technical and mathematical details behind the implementation. Instead, we'll provide an intuitive overview of some of the most important CNNs.

LeNet (1998)

The first CNN was developed by Yann LeCun in 1998, with the architecture known as LeNet. LeCun was the first to prove that CNNs were effective in image recognition, particularly in the domain of handwritten digits recognition. However, throughout the 2000s, few scientists managed to build on the work done...