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by mustafa_pasi
1839 days ago
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You can learn all you need to know in 2 to 3 university level courses. So we are talking less than a year of university courses. Fast.ai is too high level. I don't like it. You would be better served taking actual university courses. A few days ago people linked to LeCun's university class[1]. This is a solid introduction. Does not cover everything but that is OK. Seems like it is missing Bayesian approaches.
Then if you want to specialize in vision or speech or robotics or whatever, you take special classes on that topic and learn all the SOTA techniques.
Then you are ready to do research already, or apply your knowledge to build stuff. Of course you still have to learn how to do real machine learning, which involves all the data manipulation stuff, but that is learned by doing. [1] https://cds.nyu.edu/deep-learning/ |
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The youtube playlist is here: https://www.youtube.com/playlist?list=PL_iWQOsE6TfVmKkQHucjP...
Prof. Sergey Levine is REALLY good at explaining the intuitions of DL algorithms. This class also includes lectures on ML basics and very approachable assignments.
Many classes/blog posts start with describing what a neuron is - that IMHO is a super terrible way to teach a beginner.
To understand DL, one should know why we need activations (because linear models are not enough), why we need back-propagation (because we are optimizing a loss using SGD). This class is very great at explaining those things in an intuitive way. Following through I felt I built a pretty solid ML/DL foundation for myself.