I've got a question. I'm having a bit of trouble phrasing it, so please don't assume I'm against learning for learning's sake, that's not the point.
Whenever I've tried to learn about LLMs, machine learning, etc. I always come out with a bad feeling, compared to learning about software.
Theoretical courses make me feel like it's all baby steps, unless you assume a level of commitment equivalent to a math degree. Practical/applied stuff seems to require levels of compute, training data, etc. that make it unapproachable for a home hacker for everything beyond basic exercises like a handwritten digit classifier.
Now this feeling of "impracticality" is perfectly fine. It would be the expected outcome if I was taking a course on classic literature or modern art. But machine learning stuff feels like it could have room for learning-by-doing applications that aren't entirely pointless, so I wonder if there's some way to approach the topic I'm simply not finding.
Did anyone have the same experience? any tips if so?
Whenever I've tried to learn about LLMs, machine learning, etc. I always come out with a bad feeling, compared to learning about software.
Theoretical courses make me feel like it's all baby steps, unless you assume a level of commitment equivalent to a math degree. Practical/applied stuff seems to require levels of compute, training data, etc. that make it unapproachable for a home hacker for everything beyond basic exercises like a handwritten digit classifier.
Now this feeling of "impracticality" is perfectly fine. It would be the expected outcome if I was taking a course on classic literature or modern art. But machine learning stuff feels like it could have room for learning-by-doing applications that aren't entirely pointless, so I wonder if there's some way to approach the topic I'm simply not finding.
Did anyone have the same experience? any tips if so?