python_koans VS Pytorch

- python

The learn to code in 2 months boot camps are largely rubbish but self teaching the basics in something like Python (I like the Koans https://github.com/gregmalcolm/python_koans as they will reach you coding, Google skills, testing, and source control) will at least tell you if you like coding.

This reminds me of the Python Koans .. I loved doing those :) https://github.com/gregmalcolm/python_koans

After that, I'd work through something interactive. Lots off choices here, but maybe something like the Python koans: https://github.com/gregmalcolm/python_koans

When I started my current job I was given a couple of weeks to learn python. (I was a ruby dev previously). I really enjoyed the Python koans (https://github.com/gregmalcolm/python_koans) as a change from just reading docs, and something that gave me some small sense of achievement

I'm learning a new language right now and I'm finding its "koans" helpful. "Koans" are a format for tutorials that take the form of a script that runs on your computer, that you gradually fix up like Stardew Valley. There are Python koans here - https://github.com/gregmalcolm/python_koans

Not exactly what you asked for but a good addition to learning. https://github.com/gregmalcolm/python_koans

github.com/gregmalcolm - Python Koans

When I was learning Ruby for a job ruby koans was helpful. Python has a version here: https://github.com/gregmalcolm/python_koans not sure of the quality though.

PyTorch also has some support for them, but it's quite incomplete and has many issues so that it is basically unusable. And its future development is also unclear. https://github.com/pytorch/pytorch/issues/60832

TensorFlow

TensorFlow, developed by Google, and PyTorch, developed by Facebook, are two of the most popular frameworks for building and training complex machine learning models. TensorFlow is known for its flexibility and robust scalability, making it suitable for both research prototypes and production deployments. PyTorch is praised for its ease of use, simplicity, and dynamic computational graph that allows for more intuitive coding of complex AI models. Both frameworks support a wide range of AI models, from simple linear regression to complex deep neural networks.

> does PyTorch have a similar concept

of course https://github.com/pytorch/pytorch/blob/main/torch/utils/_py...

fyi should work on most 40xx[1]

[1] https://github.com/pytorch/pytorch/issues/119638#issuecommen...

Sure, right here: https://github.com/pytorch/pytorch/blob/main/torch/autograd/...

Here's the documentation: https://pytorch.org/tutorials/intermediate/forward_ad_usage....

> When an input, which we call “primal”, is associated with a “direction” tensor, which we call “tangent”, the resultant new tensor object is called a “dual tensor” for its connection to dual numbers[0].

*My post explains Dot, Matrix and Element-wise multiplication in PyTorch.

In PyTorch with @, dot() or matmul():

import torch # we use PyTorch: https://pytorch.org data = torch.tensor(encode(text), dtype=torch.long) print(data.shape, data.dtype) print(data[:1000]) # the 1000 characters we looked at earlier will to the GPT look like this

AI's Open Embrace Artificial intelligence (AI) and machine learning (ML) are increasingly leveraging open-source frameworks like TensorFlow [https://www.tensorflow.org/] and PyTorch [https://pytorch.org/]. This democratization of AI tools is driving innovation and lowering entry barriers across industries.