We will be building a very deep convolutional network, using Residual Networks (ResNets). In theory, very deep networks can represent very complex functions; but in practice, they are hard to train. Residual Networks, introduced by He et al., allow you to train much deeper networks than were previously feasible.
By the end of this project, we'll be able to:
- Implement the basic building blocks of ResNets in a deep neural network using Keras
- Put together these building blocks to implement and train a state-of-the-art neural network for image classification
- Implement a skip connection in your network
For this assignment, we'll use Keras.