Thanks, Jeremy!
David is directly involved in the research at D. E. Shaw Research. I joined the company because of his long term vision to use computing to make an impact on human health.
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Thanks a lot for this!
I was originally referring to Jeremy’s walkthroughs all the while although this helps too.
Ah my apologies! I’m afraid I don’t have a reference per say of how to go about those 
You could most likely see what notebooks he goes into in each one quickly (by jump cutting in the video) and that could help some 
Hi everyone I’m excited to have been offered the opportunity to attend these lectures with new content. I have a CS background but I work as a systems admin/DBA in my “day job”. I am interested in Neuroscience, Brains and how intelligence emerges out of “simple” components. I have done some ML/DL courses (Ng’s DL cert for example.) I tried doing the fast.ai 2019 part 1 but only got to doing things upto lecture 3 while I just watched the remaining lectures.
In my day job I really don’t have much opportunity to apply AI/DL in my field as the job entails making sure enterprise systems keep humming along without any “excitement” … ie; it gets boring after a few years of doing it
I hope to find some ways to apply this in my current line of work, but even if I can’t, since I took the first course in Neural Networks long long time ago during the AI winter, I’ve been fascinated with how “some kind of intelligence” emerges out of simple parts connected to each other, doing their thing.
So, this is kind of a hobby for me, maybe I will get some insights into how the brains work, maybe I will be able to contribute something (probably not) but I think just the journey is worth it even if it is just for satisfying my curiosity re: this subject.
I hope to see you all in the forums while we go through this course, a truly wonderful resource Jeremy has made available to the world.
All the best!
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Hello, I’m Andrei from Russia.
@yasyrev_andrei
For a long time i worked in business (distribution, construction and others). Couple years before i step side to be a full-time Dad, and now only join short time projects, as my kids take all my time for their school and exercises.
At free time (actually it only instead of sleeping) i learning DL.
It started almost occasionally about 5 years before. I started experiments with micro controllers for home automation and suddenly meet python on raspberry Pi. I find Python very interesting and i start learn it. I started code at school, it was Fortran and we use dark board and chalk for coding. Later we used punched cards and now real computer - some IBM mainframe. It was so interesting so i start to learn more. Then it was Pascal (again BIG IBM comp) and even C at at286. At Uni we don’t learn to code - i studied as engineer mechanic constructions. Once it was short experience with analog coding, it was curios but almost useless. After Uni i gone to business and for a very long time didn’t write any line of code. For business i used exel and intuition and often it was more accuracy then ours well paid analitics with Phd in math.
So i started learn Python. I like it very much. I find what a lot of things and tasks i can now do myself without “special people” and so on. Then i found what a lot of articles in my news line is about ML, DL with Python and other strange but interesting things. In 17-s i take Andrew Ng course. It was on Matlab and it was a lot of math - it scary me a lot but I did it! I understand what my brain is not rusted yet! After course i even rewrite a lot of staff to python, but still did not understand what to do with this. Later i found fastai, it looked very promising, but a cant find time for it - no time after work and small kids. And only late 18 i started part 1 3 edition course. Top - down method is rely great! After this i started understand how it works. And later at part 2 i continued dive to details. It was a lot of low level staff - i understood what i have to learn a lot! I stacked there a little because i like understand details of what i do. I do this as my hobby (yet!) so cant spent to much time for it. But now i feel very comfortable with python, pytorch and fastai (steel v1).
To better understand i started refactor and write from scratch resnet (and xresnet) and use it for imagenette/imagewooff training. Guys from [How we beat the 5 epoch ImageWoof
…] did great work, i tried to do it parallel, but they are SO FAST and active! Anyway i found some interesting things, now testing it, hope i can share it soon. And right now i just show some small trick to resnet model, that help beat current score on Woof leader board Imagenette/ Imagewoof Leaderboards. And thank to nbdev i can easy share my constructor for model https://github.com/ayasyrev/model_constructor, what i use in my experiments for easy change activations, pools and other staff. It not so powerful as xresnet in fastai v2, but hope it can be helpful for study and understanding.
Big thank whole fastai team (including forum guru) for you job and for invitation!
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@mike.moloch best to use @muellerzr’s thread dedicated to his walkthru for questions about it.
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Those are amazing goals and I’m sure you might be able to accomplish them-but I’d also recommend not stressing yourself if you cannot.
Personally, I’m still to achieve a few of the goals that I first posted on this forums-2 years ago-okay maybe I’m stupid and you might get it right in the first attempt. I’d like to post a gentle reminder to not get burned out and take your time.
A few of us-at least myself-even though the course lasts 7 weeks, take 6 month-I sometimes take longer to digest the course.
Remember: “Completing the course” isn’t the end goal-it’s to get better at DL-however you achieve that goal.
I can promise you, Jeremy won’t punish us if we get super interested in a Kaggle Comp and take time off the lectures-go gold/silver finish it (More than 10 folks have achieved this) and then come back to it later.
The field isn’t going away soon (Or So I hope) and if you feel you absolutely need to complete Part 1 and 2 before being ready-I’m yet to complete Part 2 myself. (Or have the courage to say it out loud)
I might be a bad example, but as I’ve failed or found new ways to fail with the course, here are the things that I’ve always found useful:
- Building ideas/projects/kernels/pipelines
- Kaggle Competitions: Not just for rankings but for understanding the problem and field. (Ex: Transformers via a NLP Comp)
- Working on blogposts.
Things I never found returning to me (yet) in any useful manner:
- Reading Theory-I usually forget it for most of the parts.
Okay, granted that at one point, you’ll have to digest the veggies to stay healthy but I’m not past that point yet.
So, please take your time, don’t stress out if there’s a lot to do-because there will be. Remember, it’s most important to enjoy the course, take it easy and build great things
Everyone of us will welcome your questions later or anytime-so follow your own pace 
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Hi,
my name is Jonas and I work in a research department for Artificial Intelligence at a large German manufacturing company. I already participated in (all!) previous versions of this course and have worked on some Kaggle competitions as recommended in the courses, notably the Corporación Favorita Grocery Sales Forecasting . In 2017 I visited the first ever Data Institute Conference in San Francisco and appreciated it a lot.
My work is primarily related to symbolic AI but our research department also deals with probabilistic AI and machine learning. Deep Learning is a fascinating topic and I’m looking forward to learn whats new in this course, and also to learn the new fastai library.
Thanks for inviting me again and thanks for all I learned here so far.
Jonas
Update: this is my Twitter account
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Hi, I am Yash Mittal currently working as Data Scientist in Bangalore, India. I am a fastai student and fan. I am associated with fastai since year 2018 and learned a lot. Last year I have attended all live lectures of Jermey. In year 2018 and 2019 I worked more in computer vision, and now I am learning more stuff in NLP. I have recently done Rachel NLP course. I am happy and excited for this new version of course.
Thanks Jeremy for the invitation
Linkedin: https://www.linkedin.com/in/ymittal23/
Twitter: https://twitter.com/mittaltechie
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Aren’t you just teaching the whole v4 course 
No, I certainly wouldn’t do it if I were 
it’s application focused without most of the theory (whereas Jeremy does go into the math a bit etc) And just to show some of the other techniques that aren’t focused on as much
Nikhil here from Bangalore, India. Been in the software industry for 19+ years (in Networking & Telecom domains). Have been dabbling into ML for the past 3+ years. Took a mid-career break last year to build some ML apps (specifically NLP on twitter data). Started well (BERT vs ULMFiT) but ended up doing lot of travel post that. And now I have to worry about getting back to the industry as I approach the end of my runway. Have picked up on my goal again and currently looking to train BERT from scratch for English, Hinglish & Hindi languages. If anyone wants to collaborate, kindly PM me.
Thank You to Jeremy and Fastai team for the privilege to be a part of this acclaimed group.
Twitter: https://twitter.com/NikhilUtane
LinkedIn: https://www.linkedin.com/in/nikhilutane/
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Hi all. My name is Matthijs and I’m from the Netherlands. I am a freelance consultant in the field of machine learning for mobile. In other words, companies hire me to add ML to their apps.
I’ve watched all the fast.ai videos all the way back to the very first one. I keep coming back every year because there are so many useful tips & tricks in these lectures. 
One of my goals for this year is to turn my ML / software engineering skills into a product. Consulting can be fun, but it’s time to start doing my own products again.
Twitter: https://twitter.com/mhollemans
Website / blog: https://machinethink.net/blog/
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Wow: @machinethink has also authored books for https://www.raywenderlich.com (one of the best iOS learning websites)
Matthijs, have you experimented with fastai or PyTorch on mobile? Would love to hear your thoughts on the current state of things.
Thanks in advance!
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Hi Sanyam, love your podcast. 
I have not experimented with PyTorch on mobile yet. I like the idea but as far as I know it currently only uses the CPU (at least on iOS), which is not powerful enough for the sort of thing people want to do on iOS (usually computer vision related). But it’s definitely on my to-do list.
(I do quite often convert models from PyTorch to Core ML, which is iOS’s built-in framework.)
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I’ve been thinking of this as well. Let us know when you get started building and/or need more hands. Good luck 
!
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They were pretty good. The course provided a good overview with Carlos and Emily alternating on the courses. They covered a few of the topics I haven’t seen covered in other Moocs ( Like Adaboost, Gibbs Sampling etc).
The downside was that the MOOC was tightly coupled to their framework Graphlab/Turi.
The last 2 modules were supposed to be on NN and a capstone project on Deep Learning when it stopped. And I was really bummed about it. But then fast.ai came along shortly and its been great ever since
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Thanks Jeremy for the message, sorry I was a way for the last two weeks. Here are the papers and the links:
M. Sayed and F. Baker, “Thermal Face Authentication with
Convolutional Neural Network,” Journal of Computer Science, vol.
14, no. 12, pp. 1627-1637, 2018.
https://thescipub.com/pdf/10.3844/jcssp.2018.1627.1637
M. Sayed and F. Baker, “E-Learning Optimization Using Supervised
Artificial Neural-Network.,” Journal of Software Engineering and
Applications, vol. 8, no. 1, pp. 26-34, 2015.
https://www.scirp.org/journal/paperabs.aspx?paperid=53428
N. Zaeri, F. Baker and R. Dib, “Thermal Face Recognition
using Moments Invarients.”, International Journal of Signal Processing
Systems, vol. 3, no. 2, pp. 94-99, 2015.
http://www.ijsps.com/uploadfile/2014/1210/20141210041259973.pdf
M. Sayed and F. Baker, “Blended Learning Barriers: An Investigation,
Exposition and Solutions,” Journal of Education and Practice, vol. 5,
no. 6, pp. 81-85, 2014.
https://www.iiste.org/Journals/index.php/JEP/article/view/11212
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Hi All.
My name is Sergii. Originally from Ukraine, live in Poland, work for Mountain View as Deep Learning Engineer thanks to fastai part 1 2017. We are doing lots of interesting CV/NLP stuff for e-commerce.
I was trying to attend the course in person but yesterday I was refused to obtain B1/B2 visa for studying purposes.
My main goal now is to help people who just start their journey and refresh basics.
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My understanding is that you can join the certificate course (it’s only 2 hours per week) with a plain tourist visa, as long as the main purpose of your trip is not study. I’m not an expert on this however, but this is based on I’ve heard from previous participants. When you arrive at immigration they ask you what the purpose of your trip is.
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