Signal Processing - Data Analysis with Neural Networks

Having acquired substantial knowledge on neural networks, we are now ready to perform our first operation using them. We will start with processing signals, and see how a neural network is fed data. You will be mesmerized at how increasing the levels and complexity of neurons can actually make a problem look simple. We will then look at how language can be processed. We will make several predictions using datasets.

In this chapter, we will cover the following topics:

• Processing signals
• Images as numbers
• Feeding a neural network
• Examples of tensors
• Building a model
• Compiling the model

• Implementing weight regularization in Keras
• Weight regularization experiments
• Implementing dropout regularization in Keras
• Language processing
• The internet movie reviews dataset
• Plotting a single training instance
• One-hot encoding
• Vectorizing features
• Vectorizing labels
• Building a network
• Callbacks
• Accessing model predictions
• Feature-wise normalization
• Cross validation with the scikit-learn API