# Week 2

ποΈ*Yann LeCun*

## Lecture part A

We start by understanding what parametrised models are and then discuss what a loss function is. We then look at Gradient-based methods and how itβs used in the backpropagation algorithm in a traditional neural network. We conclude this section by learning how to implement a neural network in PyTorch followed by a discussion on a more generalized form of backpropagation.

## Lecture part B

We begin with a concrete example of backpropagation and discuss the dimensions of Jacobian matrices. We then look at various basic neural net modules and compute their gradients, followed by a brief discussion on softmax and logsoftmax. The other topic of discussion in this part is Practical Tricks for backpropagation.

## Practicum

We give a brief introduction to supervised learning using artificial neural networks. We expound on the problem formulation and conventions of data used to train these networks. We also discuss how to train a neural network for multi class classification, and how to perform inference once the network is trained.

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