Practical Deep Learning for Coders, v3

The course is excellent.Jeremy is a master practitioner and an excellent communicator.The level of detail is right: high-level first, then lower-level, but all how-to, not why.Application focused rather than technique focused.If youre a deep learning practitioner or you want to be, then the course is required viewing.The videos are too long for me. I used the YouTube playlist and watched videos on double time while I took notes in a text editor.I am not interested in using the fast.ai library or pytorch at this stage, so I skimmed or skipped over the code specific parts. In general, I prefer not to learn code from video, so I would skip these sections anyway.

I really love this course. Here are some reasons why:They give intuitions and easy-to-understand explanations.They supplement their courses with great resources.They encourage you to apply deep learning to your respective domains to build things.They seem like theyre always up to date with interesting and novel publications, and incorporate them into the fastai library where appropriate.They also do a lot of research on deep learning (read: ULMFiT).They have built a community around the fastai library hence you will get support easily.Their tips and tricks are good for Kagglers and accuracy-driven modelling.

Course is amazing, library is amazing. I'm learning so much and it works on the real-world problems I'm working on so effortlessly

The theory is great but I didnt like the use of their fastai library. It should be Keras or smth. similar for beginners.

First of all, I just want to say thanks to Jeremy and Rachel for the courses. I can't say enough good things about the courses. The course may not be for everyone, but it is definitely for me. Granted, each person has their own learning style. Top-down vs. bottom-up approach: some learn better starting from theory, some learn better starting from practical application. I enjoy the approach because I can later dive deeper (reaching into the theory) to understand it better.

After watching the lectures, doing readings and going through notebooks and trying to replicate results myself I now feel confident that I can deploy a deep learning model and achieve good results. Thank you very much for this opportunity!

This course is best if you want to start building ML models right away rather than spending a lot of time learning the theory

It teaches all the steps in the neural network, tells you all the tricks and covers them quite well. It does require extra reading and additional information to fully get it. Overall it has the feel of a graduate course and I think someone without basic (as in standard curriculum for engineers) statistics, linear algebra and probability theory will struggle and get a lot less out of it.

The course is excellent.Jeremy is a master practitioner and an excellent communicator.The level of detail is right: high-level first, then lower-level, but all how-to, not why.Application focused rather than technique focused.If youre a deep learning practitioner or you want to be, then the course is required viewing.The videos are too long for me. I used the YouTube playlist and watched videos on double time while I took notes in a text editor.I am not interested in using the fast.ai library or pytorch at this stage, so I skimmed or skipped over the code specific parts. In general, I prefer not to learn code from video, so I would skip these sections anyway.

I really love this course. Here are some reasons why:They give intuitions and easy-to-understand explanations.They supplement their courses with great resources.They encourage you to apply deep learning to your respective domains to build things.They seem like theyre always up to date with interesting and novel publications, and incorporate them into the fastai library where appropriate.They also do a lot of research on deep learning (read: ULMFiT).They have built a community around the fastai library hence you will get support easily.Their tips and tricks are good for Kagglers and accuracy-driven modelling.

The theory is great but I didnt like the use of their "fastai" library. It should be Keras or smth. similar for beginners.

This course is best if you want to start building ML models right away rather than spending a lot of time learning the theory