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1. Find a paper you like/admire

2. Implement their methods from scratch (i.e. numpy not pytorch)

3. Experiment a bit, tweaking the models/algs to gain intuition

4. Repeat 1-3



> Implement their methods from scratch (i.e. numpy not pytorch)

lol this is basically impossible and completely pointless. please show me a numpy implementation of BERT or CycleGAN or deformable convolutions (note that jax != numpy). it's like suggesting implementing a kernel to someone who wants to learn about virtual memory or scheduling.

better advice would be take a paper and implement the model using pytorch without looking at their implementation and fiddle with that.




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