The right learning path

Hi everyone

Over the past 6 months I’ve completed the ML Specialization and Deep Learning Specialization courses of this platform and was wondering what would be the most coherent steps to take to move forward. Options I’m considering are:

  • Gain some more knowledge in TensorFlow following one of the specializations and, if so, which one. Haven’t done programing other than the one required for the previous courses for many years. I didn’t find it particularly challenging so far but at some point I’m going to have to dive into this more seriously.

  • Shift a bit to the NLP realm to gain some more “general” knowledge before taking TensorFlow courses

  • Take the GAN specilaization

What would be your advice? I’m also open to other suggestions of course

Thanks for your comments

Hi, @anlafuente !

That is definitely not an easy question and has a lot to do with what you want to do in your career.
If you feel like following the more “academic” path, delving into the details of state-of-the-art models, custom architectures, etc, I would recommend you to start getting used to read papers from the top conferences and journals (CVPR, NeurIPS, …). It can be quite challenging in the beginning, but it pays off in the long run because it gives you a more solid foundation.
On the other hand, a more “industry” approach would be learning MLOps or “how to actually putting DL models in production” with AWS, GCP, etc.

Those paths are not mutually exclusive so I encourage you to learn the best of both world too! In addition, everything you mentioned are also good ideas that would help you in specific branches of DL.



All three of your bullet points are good ideas.

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Thanks! Would “TensorFlow Developer Professional Certificate” be the right to start with Tensorflow or are there any others even more basic?

Thanks @alvaroramajo

I’m not sure I’m still at that level but thanks for the tips. Never thought on MLOps but could be an interesting once as well!