[icc97]

I fully agree with you that after a MOOC you've barely scratched the surface and until you're implementing them yourself then you're not going to jump into a ML job.

However personally I view the rest of the opposite way round. Getting through a course on Deep Learning takes months [0]. Then reading through Keras code once you understand the appropriate NNs is easy.

For example it takes a while of going through Neural Networks to understand ResNets. But if you understand ResNets then looking though Keras code that creates a ResNet [1] is easy.

If I want to build a NN of any sort in Keras I can just Google for it. However there's no simple Googling you can do to teach yourself NN in an easy to follow structured way.

[0]: https://www.deeplearning.ai/

[1]: https://github.com/Hyperparticle/one-pixel-attack-keras/blob...

[p1esk]

Understanding NNs is easy. Understanding, collecting, and cleaning up data is the hard part.

Also, DL != ML.

Paraphrasing "The Tao of Network Protocols": If all you see is DL, you see nothing.

[tonypace]

There are a tremendous number of people outside of programming who spend much or all of their work time collecting, cleaning up, and understanding data. Think teachers, accountants, traders - essentially everyone who spends a lot of time in spreadsheets.

[icc97]

The parent was referring to Keras which is a NN API hence why I responded talking about NN.