
Fast.ai releases new deep learning course, libraries, and book - amardeep
https://www.fast.ai/2020/08/21/fastai2-launch/
======
tosh
I do recommend getting the book that just came out (I did, it is fantastic)

[https://www.amazon.com/Deep-Learning-Coders-fastai-
PyTorch/d...](https://www.amazon.com/Deep-Learning-Coders-fastai-
PyTorch/dp/1492045527)

that said: fast.ai also released a draft of the book available here (including
the notebooks)
[https://github.com/fastai/fastbook](https://github.com/fastai/fastbook)

edit: if you can afford it, getting the book is a great way to support the
authors

~~~
amrrs
I do recommend getting the book to support authors who've put one of the best
Deep Learning content (completely for beginners) for Free of Cost. I know
that's the open source culture but someone doing it with education seems
highly appreciable to me!

~~~
jph00
Honestly we don't get much of a cut, so don't worry about it too much either
way. Although it's nice to support O'Reilly, since they were kind enough to
let us make it all available for free.

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/](https://www.hollows.org/) . They can
give sight back to many people that are blind, for around US$25.

~~~
paultopia
Thank you! This is such a great service to the community, really, all of it.

While you're here---do you have advice on the course vs the book? I'm a person
who really prefers to learn via writing rather than via video, but if there's
stuff in the course that people who use the book can't really experience...

~~~
jph00
Use both. The book will encourage you to work through the notebooks. You can
watch the first video and see if it adds anything for you - if it doesn't,
skip the rest.

They cover basically the same material. (Except that the course covers only
half the book - the rest is planned for a part 2 course later.)

~~~
paultopia
Thank you!

------
tosh
for those unfamiliar with fast.ai:

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

~~~
abraxas
Would it teach principles of deep learning in the same depth as if I slogged
through the YouTube videos of cs231n?

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.

~~~
The_Amp_Walrus
No. I think the hands-on approach of fastai would probably help you
contextualize the theory you learn in CS231N and elsewhere.

~~~
mrfusion
I’m considering something like this too. I need a way to keep it fun though or
I probably won’t follow through.

------
losvedir
Oh, neat! I went through an earlier version of the online course when I was
just trying to understand what this "deep learning mumbo jumbo" was all about,
and it was the clearest, and easiest to follow, and most interesting one
available, by a long shot! One of the assignments had you train an image
recognization model based on google image results, and after a shockingly
small amount of work and time I had a model that could distinguish a picture
of a game of Go from a game of Chess almost perfectly. That was a huge eye-
opener for me.

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?

~~~
tmabraham
Sylvain was working on fastai for Swift, but he became busy with the book and
course and also has left for HuggingFace. Jeremy has not been working on
Swift. No lectures on Swift in the course. The Swift4TF team is still active,
though Chris Lattner has unfortunately left.

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](https://github.com/FluxML/FastAI.jl)

------
cube2222
Not interested much in deep learning, but wanting to be somewhat on top of it
to understand it well, I've done a few courses and skimmed a few books which
are available.

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!

~~~
andreilys
Seconded. It was one of the few MOOC's I've actually completed because it was
so engaging and fun.

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

------
fpgaminer
A question for Jeremy, perhaps. For the longest time the fast.ai courses have
used Adam and one-cycle, at least for CV tasks. More recently Ranger and flat-
cos have been dominating the Imagenette leaderboards. I guess I'm curious if
fast.ai intends to switch over to teaching that policy instead of one-cycle?

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.

~~~
jph00
Yes it's a great combo. I think fastai is the only library that actually has
both of these built-in.

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.

------
colmvp
I took fast.ai a few years ago, and then again a year or so ago. I like their
lectures and their methodology of teaching which enabled me to meet a lot of
interesting people in my city, but I ended up just building models using
vanilla PyTorch instead of using their library as an added layer just because
it felt like they were tweaking and revamping their code so often that at
times it was kind of hard to connect the docs with the latest code.

~~~
jph00
Yeah that makes a lot of sense. It's why we took a year off from teaching to
try to make the definitive version of the deep learning framework we really
wanted - and even wrote a peer-reviewed academic paper about the design we
came up with.

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.

~~~
ggrelet
Are you Jeremy Howard? If so, thanks a lot for your courses and framework,
it’s really great!

~~~
jph00
Yup that's me. You're welcome :)

~~~
bananaface
Thank you! I also took your course recently and it was outstanding.

------
punnerud
Took this course two times, first when they used TensorFlow and afterwards
based on PyTorch. Like how it is practical from early on and updated on new
research. Recommend trying to build networks from scratch in combination with
the course, so you don’t become to dependent on the fast.ai framework.

~~~
jph00
In the new course and book by the end we show you how to build everything from
scratch - including implementing gradients, creating a data processing
library, writing all the equations out for loss and activation functions,
building a resnet from scratch, etc...

~~~
punnerud
Jermey, this sounds great! I now have several servers at our loft with GPUs,
to run training and avoid noise from the laptop. You helped me have a good
experience learning machine learning and enjoying learning+solving new
problems.

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.

~~~
jph00
Oh awesome! There's a really great fast.ai time series study group BTW, who
have between them built a lot of great projects and (IMO) are more familiar
with deep learning for time series than any other group I've come across:
[https://forums.fast.ai/t/time-series-sequential-data-
study-g...](https://forums.fast.ai/t/time-series-sequential-data-study-
group/29686)

------
burke
Bought the book and trying out the lesson 1 notebook, but man, I can't seem to
make this work. Colab can't import fastbook with the GPU runtime, and the TPU
and CPU ones are too slow. Gradient gets a little further, but fails with
"self.recorder already registered" on the "#id first_training" cell. Maybe I'm
too dumb to be a data scientist, but I didn't expect to have to do this kind
of debugging right off the bat.

~~~
unoti
> I didn't expect to have to do this kind of debugging right off the bat.

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.

------
TinyBig
This course and the accompanying libraries were very good when they were
released and have only improved over the past several years. I will echo what
others have said - the courses are very approachable and practical.

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!

------
tmabraham
People have had a lot of negative things to say about fastai v1, claiming it
is not very flexible and intuitive and only good for the certain Kaggle-type
problems. I would recommend them to check out fastai v2 as a serious
competitor to other PyTorch-based frameworks like PyTorch Lightning, Catalyst,
Ignite, etc. It's very easy to work with default deep learning problems, but
for more complex and unique problems, the mid-level/low-level API and
callbacks make it quite painless to use fastai in your workflow. Plus there's
tons of community support (forums.fast.ai + Discord), even for a package
maintained by only a few people. Check it out!

------
naveen99
Jeremy should update his JavaScript course as well. He might be one if the few
people to make it look less messy than it is everywhere else. The fast.ai
course is wonderful. Definitely recharged my own interest in deep learning.

~~~
studentinvestor
I scoured the web but wasn't able to find the JavaScript course that you're
talking about. Can you share it here?

~~~
naveen99
Here is one video on crud apps using asp.net and angular js:
[https://m.youtube.com/watch?v=Ja2xDrtylBw](https://m.youtube.com/watch?v=Ja2xDrtylBw)

~~~
studentinvestor
Thank you!

------
pandemist
Has anyone gone through a career change (to something in data science / ML)
after going through courses like fast.ai? If so, how difficult / easy was that
change?

~~~
jph00
Yes lots have - it's really common. Check
[https://forums.fast.ai](https://forums.fast.ai) for many stories. There's
also some linked from [https://course.fast.ai](https://course.fast.ai).

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.

------
mrg3_2013
This is great! Looking forward to trying it out. I explored it while back when
I was looking for a deep learning library that can take a tabular data file
and build a multitask predictive model involving different datatypes (for
example, some columns may be be text). Uber's ludwig library does it. Would
love to check it out.

------
kriro
Amazing news. I pre-ordered the book a while ago and am a bit surprised
(positively) it's over 600 pages now. The German Amazon page still says 350
pages btw.

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.

~~~
perch56
Interestingly my ordered book from bookdepository.com also says 350 pages.
Could there be 2 versions of the book?

------
Abishek_Muthian
Hi Jeremy, congratulations on the new releases and thank you.

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?

[1][http://course18.fast.ai/lessonsml1/lesson1.html](http://course18.fast.ai/lessonsml1/lesson1.html)

------
aliljet
As one of the folks that took this course, I was thoroughly engaged. I
wouldn't start masquerading as a data scientist after learning this material,
but this is a highly-practical approach to deploying new engineering tools.

------
imranq
Jeremy Howard and Andrew Ng are the two teachers who got me into ML and
eventually as a career. Amazing to see so much progress! Because of FastAi I
can see ML being used around the world just like Excel or python

------
fareesh
Looks great - will probably pick up the book

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?

~~~
jph00
Yes - the lesson on tabular analysis focuses on decision tree ensembles, since
that's what most people use (although we also use deep learning for it - and
we ended up getting a more accurate model that way). We discuss the pros and
cons of the approaches in some depth in that chapter.

Also, of course, there are many things that aren't really amenable to any kind
of machine learning...

------
aladine
Though not related to the content of post, I found that the favicon of fast.ai
is a H character, which is not related to AI. Somebody should update it.

FYI, letter H comes from theme Hyde in Hugo:
[https://themes.gohugo.io/hyde/?search-
input=menu%3Dmai#sideb...](https://themes.gohugo.io/hyde/?search-
input=menu%3Dmai#sidebar-menu)

------
lumberjack
I am trying to get into ML in general and I am having a bit of a problem. I
don't know what is what and I lack a basic trajectory. Fortunately I have all
the mathematics prereqs so I can just jump in. What I need is some sort of up-
to-date overview of everything ML so that I know what topics to study in which
order. Does anyone know of such a thing?

~~~
lexandstuff
Yes. I would recommend the fast.ai course linked above. It covers all the
essentials of deep learning and some classical machine learning. You'll have
enough breadth of knowledge to know which areas you'd like to explore in more
depth and all the skills you need to build practical projects.

------
embiggenerd
I got a little ways through the very first course way back when. I am planning
to learn ML/DS in my spare time, but I have a particular end goal - self
driving cars/computer vision. Does this course cover those topics?

------
Lucasoato
Hi Jeremy, thanks for your awesome library! I've followed the last online
course and was pretty impressed by how effective is your top-down approach.

Are multi-gpu setups supported in this version of fast.ai?

~~~
jph00
Yes, although I'm planning to work on making them simpler to use in the coming
weeks.

------
spinlock_
Any recommendation on how to approach the course? Is it better to read the
chapter in the book before you watch the lecture(s) covering the content of
the chapter or vice versa?

------
saurabp
Is there something similar for deep reinforcement learning ?

~~~
flooo
Not entirely the same, but this may be helpful
[https://spinningup.openai.com/en/latest/user/introduction.ht...](https://spinningup.openai.com/en/latest/user/introduction.html)

Previously discussed on HN: 1\.
[https://news.ycombinator.com/item?id=18408360](https://news.ycombinator.com/item?id=18408360)
2\.
[https://news.ycombinator.com/item?id=24184270](https://news.ycombinator.com/item?id=24184270)

------
phmagic
I took the fast.ai courses and highly recommend them for anyone who really
wants to learn ML.

Are there any plans for courses on reinforcement learning?

~~~
jph00
No, there isn't.

~~~
oxygenoxy
Could you elaborate on the reasons why not?

------
wasdeekrub
I wondered why fast.ai still stick with unet for segmentation task.

------
yogodojo
can anyone suggest MOOCS similar to Jeremy's teaching style? I really like the
way he teaches.

------
jph00
@dang or someone - I wonder if you can fix the title so it's not just "Fast.ai
releases new deep learning course"? The article is just as much about the
release of the fastai v2 software library as it as about the course.

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.

~~~
TallGuyShort
Thank you for all the work you've put in to your MOOC and all these other
resources. I love your teaching style and have gotten immense value from all
this.

I'm excited to get my print copy of the book delivered tomorrow!

~~~
jwuphysics
I've just received mine and I must say I find it so much nicer to learn using
the print copy book! It forces me to type out (rather than copy and paste) the
code, and I like being able to scribble all over the book. Given the thousands
of dollars of value I've gained from the courses, I have to say that spending
another $60 for the book was well worth it.

