Isaac Corley isaaccorley

Learning LLMs in 2025

So you know how the transformer works, and you know basic ML/DL, and you want to learn more about LLMs. One way to go is looking into the various "algorithmic" stuff (optimization algorithms, RL, DPO, etc). Lot's of materials on that. But the interesting stuff is (in my opinion at least) not there.

This is an attempt to collect a list of academic (or academic-like) materials that explore LLMs from other directions, and focus on the non-ML-algorithmic aspects.

Courses

David Chiang's Theory of Neural Networks course.
This is not primarily LLMs, but does have substantial section on Transformers. Formal/Theory. More of a book than a course.

	def batch_histogram(data_tensor, num_classes=-1):
	"""
	From https://discuss.pytorch.org/t/batched-torch-histc/179741

	Computes histograms of integral values, even if in batches (as opposed to torch.histc and torch.histogram).
	Arguments:
	data_tensor: a D1 x ... x D_n torch.LongTensor
	num_classes (optional): the number of classes present in data.
	If not provided, tensor.max() + 1 is used (an error is thrown if tensor is empty).
	Returns: