Implementing fully connected nets, convnets, RNNs, backprop and SGD from scratch (using pure python, numpy, or even JS) and training these models on small datasets is a great way to learn how neural nets work. Invest time to gain valuable intuition before jumping onto frameworks.https://twitter.com/dennybritz/status/961829329985400839 …
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It's perhaps clearer with a simpler example: implementing SVD teaches you how to implement SVD. Once you're done, you still don't know what it does. Using SVD on various datasets and plotting & using the results is what gives you intuition about SVD. Also, learning math
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Grad students knew how to implement neural nets in C in 2000. And they didn't have good intuition about them. A high school student playing with NN frameworks in 2018 can develop stronger understanding of NNs in a matter of days -- just thanks to a better application context
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