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RecoilS14 t1_je0x3ud wrote

I’m a new hobbiest programmer and have spent the last month or so learning python (CS50, Mosh, random Indian guys, etc) and currently also watching the Stanford ML/DL lectures on YouTube.

I have started to learn ML, Pytorch, and some Tensorflow, along with how Tensors and vectors works with ML.

I am wondering if anyone can point me in the direction of other aspects of ML/DL/Neural Networks that I may be missing out on. Perhaps a good series that goes in to length on these subjects via lectures and not just to programming side of it so I can further understand the concepts.

I’m sure there’s lots of things I’m missing out on my journey and I some perspective would be nice.

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alyflex t1_je4u0rr wrote

It really depends what you are intending to use this for. There are many sides to machine learning, but you don't have to know all of them. To name a few very different concepts:

MLOps (Corsera has an excellent series on this) Reinforcement learning GANs Graph neural networks

I would say that once you have an idea about what most of these topics involve it is time to actively dive into some of them by actually trying to code up solutions in them, or downloading well known github projects and trying to run them yourself.

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Ricenaros t1_jeax41q wrote

I would suggest picking up either pytorch or tensorflow and sticking with one of these while you learn (personally I'd choose pytorch). It'll be easy to go back and learn the other one if needed once you get more comfortable with the material.

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