Practical Deep Learning for Coders

You might get an MSc, however :P Half of my master's thesis was based on practical understanding and skills I learned from the first Fast.ai course. It's also inspired me to start a PhD in a more quantitative field to persue probabilistic models in medicine.

(Here's that's master's thesis if you're interested :) Warning, big PDF ahead: https://www.cs.mcgill.ca/~jszymb/thesis/260528685_Szymborski...)

How long did it take you to complete each course?

Do you have a recommended 'schedule'?

I have tried twice, but have not finished.

You still need a business or research case and then a dataset as any other skill out there, don’t you?

> actually a fairly complicated subject

This is heavily dependent on your background. Deep learning is essentially the simplest thing that could possibly work (parameterized nonlinear function + SGD).

If you have experience with calculus roughly equivalent to an undergrad Calc 3, which is required for most fields of engineering, the core ideas of deep learning are very accessible to you without much effort.

After that, getting it to work is mostly an empirical and experimental endeavor.

(This refers to deep learning as used in practice, not some of the current research topics).

It depends on your goal. If you want to make advancements in the field and find innovations that no one else has done (e.g. GANs and Reinforcement Learning), then a superstrong mathematical background is helpful. But if you just want to find the best output given a set of inputs and tune things for business needs, modern tooling like sklearn and TensorFlow/Keras is more than sufficient, and following a set of modern data transformation and model heuristics can get you 80% of the way there.

Medium thought-pieces/YouTubers conflate both perspectives, which has become a problem.

I've taken the first part of this (not the newest version using Pytorch, tho I'd like to retake someday soon), and found this class to be extremely useful. I do agree that in order to be optimally efficient you'd want to have a background understanding of statistics/linear algebra; however, I love this class because it gets you up and running very quickly and excited about the content.

It's also my understanding that the ML stack is making it more and more accessible to actually apply these algorithms. Sure, after you take this class you probably won't go out and invent new algorithmic breakthroughs, but many people in this class do participate in in Kaggle competitions solving real world problems (some of the assignments are submissions) and do very well, even winning occasionally.

The point of the class is to generate interest and to get people useful quickly (make neural nets uncool again), it'd be much harder to get the field to blossom if you required people to start with years of stats/math beforehand.

I think that just like most people don't need to know about the extremely complex internals of databases or networks, we should get deep-learning systems to a point where users can treat them the same way.

(Of course there's always value in knowing the details -- it helps to strive towards lowering the barrier to entry into complex systems.)

The advantage of learning lots of “deep” skills superficially is that it gives you the perspective to have a better idea of which ones you should dive more deeply into to address particular real-world problems. (It may even give you the tools to apply some of them to the real world problems they are most simply applicable to without further study, as a bonus.)

They promise to teach you deep learning, not to master it.

For many people, this is a good primer into basic concepts and how deep learning works from a high level. If they are still interested then they can work towards mastery. But you have to start somewhere.

This course is certainly not superficial. I have taken it as an international fellow. I suggest you do the first two lectures to get a feel for it.

I forget where but it used to say roughly a high school level of math understanding. You don't have to do a lot of math in the program but it can be helpful for understanding the concepts. I am very poor at math ( never did much math past high school and that ended in 2009 ), and was able to work through the course, though the course in general is not easy.

I really recommend 3Blue1Brown youtube series on linear algebra if you are struggling with some of the math concepts. They have great explanations and great visualizations.

There is a Computational Linear Algebra class taught by one of the founders of fast.ai, but I don't think it's a prerequisite necessarily [0]. I've been meaning to take it - I can't comment on its quality.

[0] https://github.com/fastai/numerical-linear-algebra/blob/mast...

Maybe just start watching it if you are interested in learning, instead of trying to put yourself in a box that you might not have the math skills needed for it.

It is free, you have nothing to lose. If the topic interests you than I am sure you will figure it out. You can always google a math concept that confuses you. There isn't a test and no one will even know.

I think they mean it in the sense that:

Theoretical foundations --> practical problems = Bottom Up,

practice without theory --> building up the theoretical legs to stand on == Top Down

That's setting the goalposts a bit narrow in terms of practical applications of ML. You can apply deep learning to solve problems aren't public facing, such as analytics forecasting.

Additionally, a lot of public ML products don't use, and don't need to use, deep learning (e.g. NLP applications).

How do you mean? As a side project? I've seen and built several ML products that are used widely inside Amazon atleast.

I have built computer vision products at my startup. I was familiar with some machine learning before, but only really learned to apply it on the job. Fast forward 7 years and the initial product and others that followed have been very successful. Machine learning is only a part of the whole product though.

Google Photos. It has great search over collections of pictures. You can search for pictures of dogs, sunsets, trees, even pictures of store receipts (which is super convenient for filing expense reports).