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)

Summary

In this chapter, we created an LSTM-based neural network that can predict the sentiment of movie reviews with 85% accuracy. We first looked at the theory behind recurrent neural networks and LSTMs, and we understood that they are a special class of neural network designed to handle sequential data, where the order of the data matters.

We also looked at how we can convert sequential data such as a paragraph of text into a numerical vector, as input for neural networks. We saw how word embeddings can reduce the dimensionality of such a numerical vector into something more manageable for training neural networks, without necessarily losing information. A word embedding layer does this by learning which words are similar to one another, and it places such words in a cluster, in the transformed vector.

We also looked at how we can easily construct a LSTM neural network in...