I am part of the Fast.ai Deep Learning for Coders Part 1 v3 MOOC whose classes are currently ongoing weekly.
After lesson 1, I looked at the Datasets built into Fast.ai now, and decided to learn by experimenting with the bird species dataset and attempting to change Lesson 1’s notebook to work with it, since it was a similar image classification exercise.
My Key Takeaways:
- The bird species data wasn’t in quite the same structure as the Pet breeds in the lesson 1 notebook, so I learnt quite a bit thru trial and error about manipulating data paths in Python to make it work, so definitely my Python aptitude is improving from this exercise. Google helped tremendously with answering questions I had no answers to when tackling the data preparation challenges.
- I always value the “figuring it out on my own” first approach, but there was at least equal value found from sharing my work in the Fast.ai forums after completing. I found at least 2 other fellow learners who had taken on the same challenge I had with the same birds dataset, who got similar results to mine. This firstly made me more confident I had taken at least a good approach to analysis as those who also shared their notebooks. Additionally, in reviewing their shared notebooks and recognising the divergence in our approaches, it furthered my learning of approaches in how they both prepared the data and applied the Fast.ai library.
- Finally, one unexpected and delightful outcome of participating and reviewing the forums is that I found a fellow Trinidadian also on the MOOC half a world away, who shared work of her own on a very Trinidad-specific problem set in image classification of masqueraders vs regular people. This got mentioned by the course teacher Jeremy in his 2nd lesson, so was a small moment of civic pride for me (and hopefully for the author as well) and is giving me confidence to tackle a similar problem-space with local (Trinidad) application just to see how far I can get.
In summary, reusing the lesson notebook with a new dataset was definitely challenging, and more than once I stopped in frustration, but came back and started again with baby steps towards completion. Once I got thru understanding how to get done what I set out to do though, and interacting with the forum community once I did, it really helped alot with solidifying my understanding of both Python data manipulation techniques and of using the Fast.ai library’s components more effectively for new problem sets.