**Topics to be covered:**

- Backpropagation
- Multi-layer Perceptrons
- The neural viewpoint

**Additional learning materials:**

Backprop Notes

Linear backprop example

Derivatives Notes

Efficient BackProp

Colah Blogpost on Backprop

Michael Nelson tutorial on backpropogation

Neural Networks and Deep Learning Book Chapter 2

MIT Lecture

**Discussion Questions:**

- Why do you need backpropagation in neural networks?
- How do you get the partial derivative on the loss function with respect to each of the parameters?
- How do you differentiate each of the various non-linearities, like sigmoid, tanh and ReLU?
- Why do we need a vectorised implementation?
- What is the Jacobian matrix?

**Hands on code implementation/Assignment:**