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)

The LSTM network

LSTMs are a variation of RNNs, and they solve the long-term dependency problem faced by conventional RNNs. Before we dive into the technicalities of LSTMs, it is useful to understand the intuition behind them.

LSTMs – the intuition

As we explained in the previous section, LSTMs were designed to overcome the problem with long-term dependencies. Let's assume we have this movie review:

Our task is to predict whether the reviewer liked the movie. As we read this review, we immediately understand that this review is positive. In particular, the following words (highlighted) are the most important:

If we think about it, only the highlighted words are important, and we can ignore the rest of the words...