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# TD2: Deep learning

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In this TD, you must modify this notebook to answer the questions. To do this,

1. Fork this repository

2. Clone your forked repository on your local computer

3. Answer the questions

4. Commit and push regularly

The last commit is due on Sunday, December 1, 11:59 PM. Later commits will not be taken into account.

The last commit is due on Wednesday, December 4, 11:59 PM. Later commits will not be taken into account.

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Install and test PyTorch from  https://pytorch.org/get-started/locally.

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``` python

%pip install torch torchvision

```

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To test run the following code

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``` python

import torch

N, D = 14, 10

x = torch.randn(N, D).type(torch.FloatTensor)

print(x)

from torchvision import models

alexnet = models.alexnet()

print(alexnet)

```

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## Exercise 1: CNN on CIFAR10

The goal is to apply a Convolutional Neural Net (CNN) model on the CIFAR10 image dataset and test the accuracy of the model on the basis of image classification. Compare the Accuracy VS the neural network implemented during TD1.

Have a look at the following documentation to be familiar with PyTorch.

https://pytorch.org/tutorials/beginner/pytorch_with_examples.html

https://pytorch.org/tutorials/beginner/deep_learning_60min_blitz.html

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You can test if GPU is available on your machine and thus train on it to speed up the process

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``` python

import torch