In this chapter, we will introduce deep models, and we will show three examples of how to build deep models. More specifically, in this chapter, you'll learn the following:
- The basics of deep learning
- How to optimize a deep net
- The speed/complexity/accuracy problem
- How to classify images with a CNN
- How to use a pre-trained network for classification and transfer learning
- How to operate on sequences using a LSTM
We will be using the Keras package (https://keras.io/), which is a high-level API for deep learning that will render approaching neural networks for deep learning much easier and more understandable because it is characterized by a Lego-like approach (here, the bricks are a neural network's composing elements).
