Hi everyone,
I wanted group’s opinion on the road map I should design for myself or I should not care about a road map. But before that, some context:
Context: I am some one who has been trying to make a transition from Project Management into Data Science. Education wise I am an Electrical Engineer and no / less coding experience. However, in last 4-5 years I have learnt coding and even real world modelling and deployment experience (classical Machine Learning).
I now want to get myself into into LLM space. However I have limited time given the full time job. I also have a side project where I have to build an text classifier i.e. given some text I have to identify if it is written by a fraudster for which my initial sense says BERT will be good. If I am able to apply and learn simultaneously, it is a win-win situation.
Now, 2 options -
- Understand LSTM, RNN, Attention, Transformer and BERT (from some online playlist or course) and then implement BERT.
- or Fine Tune BERT and then see how I can improve BERT and in the way whatever terms I see I study them topic by topic backwards.
Understanding Pytorch is required in both.
I am a learn by doing person and thus gained a lot by doing Part 1 of Fast AI course.
I have also done 1st 2 lectures in Andrej Karpathy’s course (NN Zero to hero course) which has a very similar approach to that of Fast AI.
Please advise how my road map should look like ?