it is a practitioner-style style deep learning course that instead of starting with the fundamentals starts with examples and results and then over time, layer by layer reveals what it is all about and how it works in detail until you ask yourself "that is all there is?". a great way to make a seemingly unapproachable topic approachable.
you don't need big data, you don't need a GPU, you don't need to install a ton of dependencies, you only need a browser (to access jupyter notebooks).
last but not least: this is kind of the "definitive version" of the course as it now comes with a book, a new version of the library (re-written in a more thoughtful way) and with new versions of recorded lectures/lessons based on the book w/ way better audio quality (compared to the previous ones).
If you ever were curious about deep learning but did not find the time to take a look or thought it was unapproachable: now is a great time to dive in and this is a great course (& book & library & community) to do so
Well said and this is exactly what I loved about the course and the way Jeremy peeled things back. If you're a 'learn-by-tinkering' person, and I suspect a lot of HN folks are, I can't recommend it enough.
While coursera/Andrew Ng course are(were?) classic and have great content - I personally prefer Jeremy's style and this code-first approach to Deep Learning (yes, DL != ML != AI but that's not the point).
Which version of the course is considered version 2?
A legitimate question as I'm considering embarking on one of these two paths. As most of the people here my programming skills are more honed than my math skills so the fastai path looks like the easier road to take but I'm not sure if they both lead to the same place.
https://www.amazon.com/Deep-Learning-Coders-fastai-PyTorch/d...
that said: fast.ai also released a draft of the book available here (including the notebooks) https://github.com/fastai/fastbook
edit: if you can afford it, getting the book is a great way to support the authors
Frankly though, there are much more important areas right now that could really use some extra money, so I'd rather see folks donate to a good cause, if they don't actually need the paper or kindle book... :)
I've been giving my teaching stipend from university to the Fred Hollow Foundation: https://www.hollows.org/ . They can give sight back to many people that are blind, for around US$25.
https://books.apple.com/us/book/deep-learning-for-coders-wit...
The original title was "fast.ai releases new deep learning course, four libraries, and 600-page book", although "fast.ai releases new deep learning course and library" would probably cover what most people are interested in, and is quite a bit shorter.
I'm excited to get my print copy of the book delivered tomorrow!
https://www.youtube.com/watch?v=1TfI88uQNj8 (Please seek to around 30mins for the technical part)
That was maybe 1-2 years ago at this point and I had wanted to take another look. What a perfect opportunity! And I'm excited it sounds like there might be a little more discussion of non-DL ML and applications in tabular data (where I'd have the most likely use for it), as well as the nitty gritty like deployments and use in production!
Any progress on the Swift front? Is that mentioned / used / discussed at all in this new course?
Another project that is similar is Fastai.jl, a port of fastai to the Julia language. It is still in active development: https://github.com/FluxML/FastAI.jl
So today's fastai library really doesn't have the issues that we had a year or two back - it's a really carefully designed piece of software. Amongst other things, we've made sure works with the book, which means it has to last for a long time.
Stick with it, and consider setting up your own machine instead of trying to use Colab. I say this because literally the hardest part about the previous course for me was getting started and doing the setup. Once you're able to actually run the notebooks I promise it'll get much easier. I can promise this with confidence because the lectures are excellent, and I've been through what you're talking about on the previous version of this course when I was first starting with AI.
EDIT: I just tried the Colab notebook and it worked successfully for me. We can discuss on the forums if you want.
My output from my new powers are several papers together with others and solved problems within the green tech energy market. We detect and forecast usage within timeseries data (energy consumptions).
Keep doing what you are doing! And thank for all the hours you put down into this.
Are there any plans for courses on reinforcement learning?
The fastai video course was, with a big gap, the best, most understandable, most practical and most enjoyable of them.
Just wanted to say this. Thanks so much for creating it and regularly keeping it up to date!
I hope the top-down style of teaching spreads because for some people (such as myself) it's one of the best ways to learn and get excited about a subject
I guess more generally I'm curious what criteria the fast.ai team uses for deciding what techniques to teach. My feeling is that the courses have always taught the training techniques that are a healthy mix of SOTA, generally applicable, and easy to use.
Ranger + flat-cos has seemed like a really robust combo, and easy to use. So yeah, just interested in whatever internal discussions fast.ai may have had about it and other potential replacements for Adam + one-cycle.
However, because the LR warm-up is built-in to Ranger, it's actually a bit more fiddly to use - i.e. you really need to understand what it's doing. It doesn't work great with `Learner.fine_tune` and gradual unfreezing more generally, since you don't really want a full separate warmup for each phase.
So I don't see it becoming the default or main optimizer shown in the course. But it's great to learn and use.
It's a lot of work and requires tenacity - the same amount of tenacity that's required to reach a high level of competence in any field.
Worked with fast.ai for a couple of projects starting <1.0 and with the first MOOC. You're doing great work and it's really appreciated.
In Lesson 1 they talk about use-cases where Deep Learning is the best known approach. Are there any popular use-cases for which it is not the best known approach?
Also, of course, there are many things that aren't really amenable to any kind of machine learning...
Are multi-gpu setups supported in this version of fast.ai?
Previously discussed on HN: 1. https://news.ycombinator.com/item?id=18408360 2. https://news.ycombinator.com/item?id=24184270
Fast.ai changed the course of my career and helped give birth to deep learning as a practice at my place of work. Thank you Jeremy!
I see that the original ML course[1] link has been removed from the home page. Does it mean it's been invalidated due to integration of ML lessons with the DL courses?
I was pointing those who wanted to learn ML but don't have good access to proper Internet to the old ML course with custom scripts to make installation of requirements for those course in inexpensive SBC like Jetson Nano or similar. I was planning to make those setup public, but should I refrain from doing that because of Fast.ai v2? If so, is the cloud compute de facto first class citizen now?
FYI, letter H comes from theme Hyde in Hugo: https://themes.gohugo.io/hyde/?search-input=menu%3Dmai#sideb...
