Hi,
I am bit confused about the goals/objectives of each lesson. At the end of each lesson, I do my best to replicate/run the lesson code taught by Jeremy and get a good understanding of the concepts. However what are participants doing at the end of each lesson to gain a solid understanding.
These are the recommended tasks as I understand them:
go through the notebooks presented in class, read the extra text that is in the notebook and try changing the hyperparameters to better understand them
choose a dataset of your own and replicate the notebook
If you find the Kaggle competitions too intimidating - I certainly still do - make sure you check out the kernels that people publish there, they are a great starting point.
If you manage to run the notebooks and experiment with different datasets, even if you don’t get around to kaggle competitions just yet, I think you are onto a great start for the first pass of the course. I am on my second pass right now and hoping to fill in the gaps from the first pass.
BTW there is a wealth of info on this in the first couple of minutes of lecture #1 - would recommend you check it out.
@reshama@ecdrid@radek: Great thanks for the inputs and responses. I have played around with the data but think I need to embed the concepts and gain a better understanding