Week 6 - Balt 4364 - PyTorch

 



In this chapter, I dove into PyTorch, a library I’ve personally found way more flexible and intuitive than a lot of the options out there. It’s strongly connected to the fast.ai community, which I highly recommend if you like hands-on learning without getting buried in unnecessary math. Their approach makes concepts feel practical and relevant, especially with all the rapid changes happening in AI right now. PyTorch centers around tensors—basically multi-dimensional arrays similar to NumPy, but with the power of GPU acceleration. Creating and manipulating tensors is simple, and PyTorch makes operations like addition or multiplication feel natural. To put everything into practice, I walked through a hands-on exercise using the Boston Housing dataset. I loaded the data, standardized it, built a simple neural network with one hidden layer, and trained it using MSE loss and SGD. By the end, the model could predict housing values, and it was easy to tweak things like the architecture or learning rate to improve accuracy.


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