Aim is to build very deep convolutional networks, 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 practically feasible.

Steps:

• Implement the basic building blocks of ResNets.
• Put together these building blocks to implement and train a state-of-the-art neural network for image classification.

We build ResNet 50 model using Keras and use it to perform Image Classification on SIGNS dataset.