Next steps after Deep Learning

Hello,

I’ve just completed the deep learning certificate series and am procrastinating from applying for jobs. Looking for up-to-date suggestions to build on the knowledge learned here. With so much ML in the news, there must be cutting-edge technologies and topics to research!

Seems like a natural next step to learn PyTorch / TensorFlow. Any recommendations for learning either are welcome.

As for projects, I’d like to start with music generation. The “Jazz Improvisation with LSTM” project was great and I’d like to dive fully into that.

All suggestions are welcome - thanks in advance!

-Alex

There are a number of specializations about TensorFlow that you can take here. I haven’t taken those, but with DLS in your toolbox you’d be ready for those. PyTorch is also a great thing to know and to have on your resume. It has been popular in the research community as an alternative to TF, but I think it’s gaining popularity in industry as well. The only specialization here that uses PyTorch is the GANs specialization. GANs are very interesting in their own right, so that would be something to consider. Knowing more frameworks is like knowing more programming languages: you want to have a good list on your resume so that you have a better chance that you know the tools that a given job would require.

In terms of the currently hot technology, there are a bunch of short courses about LLMs and ChatGPT. You could explore some of those. The other specialization that addresses the underlying technology used to build LLMs is the NLP specialization. I’ve only taken the first course of that and it’s the Attention Models and Transformers in the later courses that are where the action is for LLMs. Have a look at the syllabus for the NLP specialization and see if that grabs your interest.

1 Like