These notebooks are based on the FastAI v3 part 1 course. http://www.fast.ai

To get a better understanding of the notebooks. I try as much as I can to apply these notebooks to different datasets. Sometimes by joining a Kaggle competitions sometimes by searching on https://toolbox.google.com/datasetsearch. Here are some of my efforts.

You can run all these examples directly on Google Colab.

Lesson 1: I made a classifier to determine if a specific paintings is from Rembrandt, Van Gogh, Leonardo of Vermeer.

Lesson 3: This time I built a NLP application to classify if a SMS message is Ham or Spam. Thanks to this Kaggle Dataset (https://www.kaggle.com/uciml/sms-spam-collection-dataset)

Lesson 4: When you start with machine learning on Kaggle the challenge is to built your first model on the Titanic dataset https://www.kaggle.com/c/titanic. You have to predict which passengers survived. A Neural Networks didn’t give me the best results, but it was a nice exercise to play with the tabular notebook.

Lesson 5: This is a copy of a Kaggle Notebook https://www.kaggle.com/aakashns/pytorch-basics-linear-regression-from-scratch to get a better understanding of the pytorch basics (Loss, gradient descent, backpropagation), by building a linear regression model on a really simple dataset.