
TensorFlow.Js a painless way to get started with DeepLearning :) - Langhalsdino
https://blog.understand.ai/tensorflow-js-a-painless-way-to-get-started-with-machine-learning-470c15f0f637
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ralusek
I tried it, very painless.

Playing around with only a cursory understanding of neural nets, it confirmed
a lot of my suspicions: the libraries have gotten good enough that a failure
to understand the underlying mechanics and math can still get you like 70-80%
there.

If you have a base understanding around concepts like "activation functions,"
you may have gotten hung up on the precise reasoning or benefits around
choosing a particular function over another. After playing around with some of
the examples they've provided, I tried changing activation functions between
sigmoid, tanh, and relu, got virtually identical results (with relu being the
best). This same general pattern of seeing only marginal differences continued
as I tried adding additional dense layers to the network, mostly similar
results, just slower and less generalized. I tried changing filter sizes on
convolutional layers for the couple image things I tried, very forgiving as
well. It really felt like there is a very standardized solution to most types
of problems, and the iteration that goes on to improve results is more
arbitrary tweaking than it is based on any underlying theory.

