Practical Deep Learning for Coders 2018

Yeah there's a discussion of this on http://forums.fast.ai if you want the details. Basically, Andrew's course is bottom-up (i.e. start with the theory and math and simple examples) and works up from there. Ours is top-down (start with code to train world-class models and dig deeper and deeper in to how they work). fast.ai also has more examples of more recent techniques, and covers a wider variety of applications. Students who have done both tell us that they found both helpful to their understanding of the field and practice.

I have no questions, but feel compelled to say thank you.

I've been a ML/DL practitioner for the past five-plus years, and first watched one of your lectures a bit over a year ago. All I remember thinking is that a wealth of practical knowledge that had taken me years to acquire was there for the taking, for free, for anyone who cared to look.

Since then, I have been recommending these courses to anyone who asks me for advice for learning about deep learning. You -- and Rachel Thomas -- have created by far the easiest and fastest path for a wide range of people to gain deep learning expertise.

In fact, I'm so sure the new lectures will contain valuable nuggets of know-how that even though I consider myself pretty knowledgeable about deep learning (and an expert in my narrow domain of interest), I will make it a point to find time to watch all the updated lectures.

Kudos!

Hi Jeremy,

I too want to say thank you, even though I have only started with the material. Your philosophy on how to teach the subject I feel will be much more useful to me than the classes I was signed up for recently (and lost interest in rather quickly). It's a lot easier to get excited about the practical things surrounding the topic which is why I'm looking forward to diving into the new content.

On a related note, one thing I do see a heavy emphasis on with the material is on the Computer Vision / Image Processing side of things which is certainly understandable considering how popular that area is specifically right now.

Something not really computer vision related though that I'm curious about (and I'm not sure if it's covered in the new/existing lessons) would be on how to craft a data set using data I might have accessible to me, but which isn't necessarily image-based, and to apply these techniques to that sort of data set to come up with predictions (I bring this up, because one of the goals I have for learning about this topic specifically is to see how I might be able to apply it back to my job at a community college and if I can pull historical data related to our students and use that for forecasting / recommendation purposes and create some useful applications our students can utilize...as a simple idea one example would be using historical data about the current student, and maybe data from other similar students, to predict success in a student's upcoming courses).

Thank you and keep up the awesome work (and for sharing it freely :-)!

Really looking forward to getting stuck into this course. I’ve a background in fluid mechanics, in particular writing code (in Matlab but more recently with python) to identify structures in experimental and numerical data. In essence this is very similar to classical image analysis. Looking to machine learning seems a logical next step. I’ve been working through Andrew Ng’s course and while it is very good it suffers from fundamentalitis to a degree. However I do plan on completing Ng’s course first.

Sweet! Thank you!