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)

Results analysis

Having successfully trained our MLP, let's evaluate our model based on the testing accuracy, confusion matrix, and receiver operating characteristic (ROC) curve.

Testing accuracy

We can evaluate our model on the training set and testing set using the evaluate() function:

scores = model.evaluate(X_train, y_train)
print("Training Accuracy: %.2f%%\n" % (scores[1]*100))

scores = model.evaluate(X_test, y_test)
print("Testing Accuracy: %.2f%%\n" % (scores[1]*100))

We get the following result:

The accuracy is 91.85% and 78.57% on the training set and testing set respectively. The difference in accuracy between the training and testing set isn't surprising since the model was trained on...