my advice is to proceed. its cool to know the math underneath, but just go implement stuff dude, if it doesn't work you can always remote/rent GPU. what i did for my thesis is google tutorials and re-implement them using my dataset. through all the bugs and the elbow grease, you will know enough to at least speak the language. just do it and don't procrastinate with these types of posts (i do this too sometimes)

EDIT: a lot can be done on colab these days regarding neural networks and huggingface. google huggingface documentation! i implemented a huggingface transformer model to do audio classification (and im a total noob i just copied a tutorial). it was total misuse of the model and accuracy was bad, but at least i learned and given a real problem i could at least find my way forward.