# Week 3 - Intro Kaggle competition - EDA and baseline models with PyTorch

## Course session

{% embed url="<https://drive.google.com/file/d/18VsrKSqNFaWeWsL24ULFrNnwpghpdwJZ/view?usp=sharing>" %}

[**https://drive.google.com/file/d/18VsrKSqNFaWeWsL24ULFrNnwpghpdwJZ/view?usp=sharing**](https://drive.google.com/file/d/18VsrKSqNFaWeWsL24ULFrNnwpghpdwJZ/view?usp=sharing)

**Walk-through**

Hyperparameter experiment

The following notebook will show how to set up a hyperparameter experiment in plain PyTorch. More importantly it give you the results and enables you to analyze and play around

{% embed url="<https://colab.research.google.com/drive/1QNIM08JzPF0757GLODwvo-xtta3wqQo9?usp=sharing>" %}

**Kaggle**&#x20;

* Introduction
* Titanic

**Solutions exercise MLP**&#x20;

Presentation from the participants of the MLP from Coursera

## **To-do**

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Watch the videos on the next page

Go through the following notebooks and complete the second one (assignment notebook):

{% embed url="<https://colab.research.google.com/drive/11zzm21ctQVyzzy7uZmEYQnGwD05Xy0F8?usp=sharing>" %}
The first notebook
{% endembed %}

{% embed url="<https://colab.research.google.com/drive/1BogNxeMD8OGfh55gTf47vEdSNauQyZjL?usp=sharing>" %}
The assignment notebook
{% endembed %}

The next task is to analyze the results of the hyperparameter experiment and create a small presentation on your findings(e.g. batch size of 16 with lr=0.2 seems to equal batch size of 1 with lr=0.01). Here is the notebook again:

{% embed url="<https://colab.research.google.com/drive/1QNIM08JzPF0757GLODwvo-xtta3wqQo9?usp=sharing>" %}

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Run your own hyperparameter experiment

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Do your own EDA on the Titanic Dataset and/or look at other EDA notebooks from competitors. Make a final presentable EDA notebook.

Familiarize yourself with this PyTorch Tutorials:

{% embed url="<https://uvadlc-notebooks.readthedocs.io/en/latest/tutorial_notebooks/tutorial5/Inception_ResNet_DenseNet.html>" %}