How to practice DL after graduating the specialization?

Course Q&A Deep Learning Specialization Convolutional Neural Networks
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Dear mentors, fellow students…

  1. I’ve just started course 4 after graduating courses 1-3. I’m a little concerned, because apart from theory, which is important per se, what I learned to do in up til now is to fill out some chunks of code. It’s not as if I am now able to write the full code of a DL project, or to use a framework such as TensorFlow, as we only had a short glimpse of it at the end of course two. I am starting to get concerned about the question: how to practice in order to become a real DL practitioner as soon as I graduate courses 4-5? Maybe you address the issue later in the speciaization, but I would like to get an answer that will calm my concerns. Where to start?

  2. Personally I would like to work in the sphere of NLP, as I have a PhD in Linguistics (from the faculty of Humanities). I know there are a few courses in coursera about NLP, but which course would you recommend me to take after graduating this speciaization?
    Thank you,
    Doron

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Hi, @Doron_Modan !

The best advice I can give you is to practice with Kaggle competitions. That’s probably one of the best platforms out there for ML/DL projects with real world data.

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You will get lots of practice using TF in courses 4 and 5.

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As Tom says, you’ll get plenty more exposure to TF/Keras as you work your way through DLS C4 and C5. If your primary interest is NLP, you’ll definitely want to get all the way through DLS C5: that covers the types of models that are the most powerful for NLP applications. Once you finish DLS, then you have a number of options. If you feel the need for more TF experience, there are a number of specializations which focus on that. Then there is also the NLP specialization. That gives a lot more of a survey of NLP techniques than you get in DLS C5. NLP covers a lot of other techniques before it gets to Sequence Models and Attention, which are the subjects of DLS C5.

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In my personal experience, the moment I found myself really remember how to use (some of ) tensorflow is when I worked on my projects, during which I researched a lot on how others implemented their models. It is a process that I had to keep reading others’ code, keep polishing mine, and keep trying something new. A single or two courses never got me there.

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My suggestion is that you can search Github where you’ll find thousands of projects that you can study, reproduce and apply what you have learned here.