# Machine Learning Notes

These are my notes made during watching Andrej Karpathy’s tutorials on YouTube. I have also more theoretical (overview) notes about NLP and ML.

The tutorials are these:

## (Py)Torch

- Tensor
`.view(<shape>)`

will take the internal linear (memory) representation of tensor and layout it according to the wanted shape- very efficient (better than e.g.
`torch.cat(torch.unbind(tensor, 1), 1)`

- very efficient (better than e.g.

`arange`

similar to`range`

in Python`randn(<shape>`

will fill with numbers from normal distribution`-1`

in shape tells torch to infer the dimension- squeeze
`torch.linspace(from, to, steps)`

is like`range`

in Python but works for floats- sum per row:
`P.sum(1, keepdim=False)`

- broadcasting semantics
- from numpy
- when a binary operation is defined for two tensors:
- both dimensions are equal
- one of them is 1
- one of them doesn’t exist

`toch.nn.functional.one_hot`

- common way of importing functional:
`import torch.nn.functional as F`

`@`

is vector multiplication- indexing with a range:
`x[torch.arange(10), y]`

`with torch.no_grad(): ...`

tells torch to not include what follows in backpropagation`torch.zeros_like(tensor)`

will create a new tensor with the shape of`tensor`

with all zeros`torch.all_close(t1, t2)`

will compare tensors with some tolerance

published: 2022-11-24

last modified: 2023-11-20

https://vit.baisa.cz/notes/learn/machine-learning/

last modified: 2023-11-20

https://vit.baisa.cz/notes/learn/machine-learning/