New course on deep learning

Heya friends!

As some of you know, I’ve been working on a deep learning course for a really long time. I’m sorry you had to wait so long, but I hope you find it’s worth the wait :wink:

Here’s the link, with discount coupon:

The course is a rigorous deep-dive into deep learning, with many foundational and advanced topics covered (and I have a few more course topics planned for the coming months). The entire course is in Python (using the PyTorch library), so I’ve included an 8-hour Python intro for those who are less experienced with Python (lookin’ at you, MATLABers!).

Whether you want to enroll is up to you, of course. But I definitely recommend visiting the course page and checking out the preview videos.

As always, happy learning! And stay safe.



Hi Mike,
There is any application of Deep learning on EEG signals in this course.

Hi Hamid. Well, there aren’t any examples of deep learning on EEG data, but you could use the code for EEG, hopefully without too much adaptation.

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Hi Pranjal.

  1. Well, deep learning is a big and ever-increasing field. It’s not possible to include everything. I tried to include enough of the fundamentals such that any other DL topic should be easy to learn (indeed, most modern applications of DL are simply clever ways of combining existing architectures). There are also several coding languages and environments for DL; I have a lecture in the course about why I chose PyTorch. Fortunately, the differences between PyTorch and TensorFlow are only syntax; the underlying methods, models, parameters, etc, are the same.
  2. Sorry, I don’t think I can do that :wink: My recommendation is to search the web for topics that you find interesting, and see what’s been done in the DL sphere on those topics.


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Hello Dear Mike
I am a Master’s business student and want to use deep learning in my Master’s Thesis. My thesis is about emotion detection from third-party website reviews. After categorizing emotions, I should analyze behavior in a time series for each business and as a whole dataset.

With these descriptions for my thesis, I have some questions:

  • the deep learning udemy course is suitable for me or not?
  • if yes, should I complete all courses, or can I skip some parts? I think the CNN part is related to pictures, not my work.
  • do you have any suggestions for me to learn time series analysis better and complete my thesis? I am really confused about how to aggregate reviews of businesses and analysis 3000 businesses and 50,000 reviews?!!! No idea about that

Best Regards, Erfan

Hi Erfan. Indeed, sentiment detection in reviews is one of the applications of text-based DL. I don’t that application per se in my course, but you’ll certainly learn the math and mechanisms of DL that apply to any kind of model architecture.

My advice is to enroll in the course and have a look through it within 30 days. If you think it isn’t right for you, then you can get a refund from Udemy. I promise I won’t be offended if you do that :slight_smile:

And yes, you can skip parts of the course that are not relevant for you. The first few sections are general and apply to all DL models, while the later sections become more architecture-specific.

Hope that helps!

thank you very much. i hope see more deep learning course from you and NLP
I really like your teaching style (I watched one of your free course about academia on udemy)

you are really great teacher

create more course :slight_smile: :heart:

Thanks :slight_smile: Next up is calculus. That should be published within a month.

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