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 IMDb movie reviews dataset

At this point, let's take a quick look at the IMDb movie reviews dataset before we start building our model. It is always a good practice to understand our data before we build our model.

The IMDb movie reviews dataset is a corpus of movie reviews posted on the popular movie reviews website https://www.imdb.com/. Each movie review has a label indicating whether the review is positive (1) or negative (0).

The IMDb movie reviews dataset is provided in Keras, and we can import it by simply calling the following code:

from keras.datasets import imdb
training_set, testing_set = imdb.load_data(index_from = 3)
X_train, y_train = training_set
X_test, y_test = testing_set

We can print out the first movie review as follows:

print(X_train[0])

We'll see the following output:

[1, 14, 22, 16, 43, 530, 973, 1622, 1385, 65, 458, 4468, 66, 3941, 4, 173, 36...