The similar information is shown under “arxiv_primary_category” column
n=10000 is a big enough number so I don’t need to change it all the time. But, I definitely have more than 120-130 items in computer science. You can check the details easily by using Pandas.
@jeremy Can you please suggest the best way to go through the course?
I have a couple of questions in mind:
Do I try to fully understand a concept and write the simple algorithms from scratch as and when it is discovered in the course or should I go about just running the code cell and wait till later when it is done in more depth?
Is every concept like cosine annealing, stochastic gradient descent with restarts, data augmentation revisited or these should be done by ourselves?
@ other learners or people who have completed the course, please do share your approach too.
I think best way is to watch each video weekly,Then dig out all the python codes @jeremy has provided try to see what each line of code is doing.Make some changes get more accurate results and complete the course.
On using load_learner, I got the following error, any ideas on how to resolve the issue.
in
----> 1 learn_ett = load_learner(Path(’/baseline-b4’))
/opt/conda/lib/python3.7/site-packages/fastai/learner.py in load_learner(fname, cpu)
551 "Load a Learner object in fname, optionally putting it on the cpu"
552 distrib_barrier()
–> 553 res = torch.load(fname, map_location=‘cpu’ if cpu else None)
554 if hasattr(res, ‘to_fp32’): res = res.to_fp32()
555 if cpu: res.dls.cpu()
AttributeError: Can’t get attribute ‘NonNativeMixedPrecision’ on <module ‘fastai.callback.fp16’ from ‘/opt/conda/lib/python3.7/site-packages/fastai/callback/fp16.py’>