Fast.ai Part 2: Deep Learning from the Foundations (opens in new tab)

Been starting your DL course and it's amazing. I love how your approach is both ambitious and practical at the same time.

You mentioned you studied philosophy as an undergrad. How did you end up working with analytics and DL?

(I know this is an old thread but I couldn't resist)

Why would you need Swift in Jupyter?

Just implement your model's architecture in Python. Export it. Invoke it at runtime via Swift and TF.

You might find it helpful to see what other people are doing with deep learning - see if any of it seems relevant to your interests. For instance, here's some examples of folks from diverse backgrounds using DL in their domains of interest: https://www.fast.ai/2019/02/21/dl-projects/ . Or here's a rather deep rabbit hole - hundreds of replies from people showing their learning projects: https://forums.fast.ai/t/share-your-work-here/27676 .

I find it useful to think of DL as just another way to get computers to do what you want. Rather than focusing on control flow and setting/reading variables, you focus on providing examples to learn from. Both approaches can do many of the same things, but each has areas that they're better at. Eg DL is better for things that are hard to explain just how you do them (e.g. seeing pictures, hearing sounds, reading text) and traditional coding is better for things that need specific logical steps. A combination of the two is often best for solving end-to-end problems in practice.

I agree with looking at projects that other people have done. The other thing I would say is: just do part 1 version 3. That will give you a really good idea of what is currently being done in this area and if it isn't interesting to you at that point, you will at least have enough information to know that for sure. Make sure you don't just watch the videos though. You have to actually implement something after each lesson.

Sometimes it is useful to make useless things. It will build experience while being fun and making you more able to come up with "useful" things. Tried this with a friend of mine. Our plan was.

1. Take webcam at desk. 2. Build model that detects whether he himself is sitting at his desk. 3. If it is not him, you've detected an intruder, so spray the intruder with a squirt gun or something.

We only got as far as a model that distinguished between him and other people, even deploying it according to this guide. https://course.fast.ai/deployment_render.html

Also the material is just really interesting. If you want to know how a lot of products work, this is one of the most fun ways to learn.

You might come up with use-cases after going through a few of the examples. You know that expression, "when the only tool you have is a hammer, everything looks like a nail"? Well, I think you can use that to your benefit. Once you learn how to swing the hammer of ML/DL, then you'll start seeing nails everywhere.

Swift is quite different from Python.

I have taken all of the fast.ai courses available and they have been life-changing for me. The style of teaching that Jeremy uses really clicks with my learning style (Code then explain) and I have gone from knowing nothing about machine learning to finishing pretty highly on a few different competitions mostly involving NLP and Computer vision. I have been working on Super Resolution more recently and I started working as a Data Scientist at work which has been great. I actually didn't even have a ton of programming experience when I started fastai 3 years ago, but now I am pretty confident when I need to code something. I feel like I have moved through the stages from script-kiddie of data science to a point where I can actually intuitively understand what is happening and trust my instincts to change well-established architectures in ways that it works better for the problem that I am focusing on.

I was lucky enough to participate in the lectures earlier this year. I've watched every fastai course before this and I will continue to watch them, even the introductory material.

That being said, this course is completely different than anything done before. This course takes a look at the very most basic functionality of neural network libraries and proceeds to implement the fastai library totally from scratch. So it's really a great deep learning + tooling + api design + programming language + research implementation course all in one.

The last two lectures on Swift were especially fun. Python isn't great for deep learning because you always have to live at arms length from the implementation through a c++ wrapper. Swift can compile straight to the low level system commands. LLVM, MLIR. Still learning this so I'm not the best person to answer questions about this specifically. :)

So Swift has this awesome feature set of being well designed, pretty similar to Python, statically typed, and low level. These lectures are hosted by Jeremy Howard and Chris Lattner. Sometimes the most valuable learning I can do is to just listen to the banter between two exceptionally experienced experts, so this make the course that much more enjoyable.

Five starts would recommend. Get the t-shirt.

I started using the techniques immediately, for applicable deep learning and NLP projects. There's no need to finish the entire course to get some actionable rapid prototyping done. Each lecture is nearly self contained once you're comfortable with the basics.

The content is very well paced and engaging. The use of Jupyter Notebook really is a great way to demo code. I took Part 1 online (free) and then Part 2 in person ($2000) and feel the money was well spent. TBH, I haven't used it yet, but I enjoyed the classes (they were exciting at times) and I do have a project I'm working towards where I'll use this.

I've gone through all previous iterations of their courses, so have familiarity with using pytorch and the fastai library, but haven't done the best at keeping up with their rapid pace. This course helped me understand the building blocks of the library, which in turn helped me out in a few projects.

For one project, I adopted what Jeremy taught about building code for processes, which our team has then used for a couple other things as well. For another project, I was able to easily put together the code for training on a set of data with a somewhat complex structure.

Not in the short/medium term, at least. The stuff we covered in that Intro to ML course hasn't really changed, so the current course is nearly equally relevant today. (We used the fastai library a little for some basic utility functions, and that bit has changed, but that's not a very important detail.)