Hands-on roadmap for deep learning

I have completed the first four courses. Decided to review them before doing the final one. I am trying to build my toolbox – if there is a problem I need to solve, how do I get started.

Andrew has recommended start simple, start with Logistic Regression then buuild from there.

The projects in Course 2 seem to include all the recommended best practices. Seems that might be a good place to start.

Would appreciate any guidance.

Also references to complete solved model projects. I see a few on Coursera. There may be other resources.

Thanks.

Hi, @dds !

That sounds really good and that’s the kind of thing I would do in your situation. Having a clear view of everything you have studied helps a lot in the future when you put it in practice. Of course, everyone has some preferences, but I would recommend you one thing that is not exclusive for deep learning: learn how to use git and start building your “portfolio” of tools and projects. Not only to have everything organized, but to showcase what you have done.

For deep learning related recommendation I would suggest Dive into Deep Learning book

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Wow this is a great resource! Thank you for the recommendations!

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