Absolutely - it will happen. Not in this part though, sorry! I want to wait until we can do a really good job of it. Pretty sure the next course will be the one…
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Hi,
I am Ajaykumaar, I am doing my B.Tech in EEE. Fastai, being my very first course in Deep Learning, it greatly helped to understand Neural nets from scratch without having the “this is a magic” feel. Fastai’s user-friendly syntax and never failing defaults have made deep learning a lot simpler and efficient.
I use fastai to implement deep learning in different fields like, to predict chest cancer or diabetes, NLP in Tamil language and also learning to implement deep learning models in independent systems, for instance using Raspberry Pi. It would be great if the 2020 course covers deploying models as a stand-alone system.
Thank you @jeremy for the invitation to the 2020 course. It is great to be a part of Fastai’s forum.
Thank you,
S.Ajaykumaar
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Hi everyone,
I’m computer science student from Finland. I have followed what’s happening in fastai since the last course but because I started college and now joined a startup it’s sometimes hard to find time to talk here and read comments by other people. But as Jeremy hoped in the message I try to take the habit of reading and writing here before the course starts.
People have been really thankful for my notes from past courses and it always makes my day to hear nice feedback. Personally I’m never satisfied with the quality of the notes after writing them but I’m happy to hear that they help other people. The plan is to again write notes because that’s a great way for me to learn but I hope that other people also could write their own versions which are hopefully even better than mine.
I have seen Jeremy talking on Twitter about how to create own blog but I probably use something else. Medium is not even an option anymore because they have the paywall thing which drives everyone crazy. I’m planing to use Notion because I have moved most of my notes from last year there too and the plan is to keep it as public notebook. I like some of the features they have and that’s why I’m sticking with it instead of creating own blog.
Anyways I’m excited that the course will be starting soon.
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I’m thrilled to hear that - thanks!
Note that with fast_template you can blog directly from Jupyter Notebooks now! 
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Hey everyone, I joined about a year ago and worked through the 2019 versions of the course last year. Since then I’ve been working on Kaggle competitions and working with other members of the fastai community on various projects.
I have two bronze medals on Kaggle and would like to continue improving this year. If anyone would like to team up I’m open to that. Currently I’m working on the Google Q&A Challenge which wraps up in ~two weeks.
I stream my work on Twitch every weekday at 2pm EST and 9pm EST. Stop by if you want to chat about fastai, Kaggle or deep learning!
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Hi everyone!
My name is Akash. (https://mobile.twitter.com/akashpalrecha98) I’m a third year Mathematics student at BITS Pilani, India. I’m also the AI Lead for a space tech startup called Pixxel .We build small satellites that will capture high resolution Hyperspectral Imagery of the earth, something that hasn’t been commercially available before. We were incubated at NASA/Caltech’s Jet Propulsion Lab last year for 3 months. Our first satellite launches in June 2020!. At this point, me and my team are just starting to teach ourselves GeoSpatial Deep Learning)
I started my coding / deep learning journey back in the summer of 2018 with Andrew Ng’s ML course on Coursera. In June 2018 I started doing FastAI’s Deep Learning course but was quickly bottlenecked by my lack of experience in coding / python / numpy etc. I took a sabbatical from the course for a while to level up my python skills, took CS231n and got back to doing FastAI in the middle of my 3rd semester in college.
I made a lot of mistakes while following the course in 2018, like not trying to reproduce the notebooks, trying to move very quickly through the course, etc.
In the 2019 version, I tried to correct all of that and made sure I deeply learn everything that Part 1 had to offer.
I also headed deep into Part 2 of the 2019 course (which I am still doing, at lesson 11-12). I am doing this part of the course at a deliberately slow pace. My objective not being to complete it, but to learn the essence of the process that Jeremy takes us through. Instead of reading just the research papers that Jeremy outlines during the course, I’ve built a habit of following all the latest AI research and reading new literature regularly. I’ve also tried working on 2 of my own research projects and the process of troubleshooting them, getting those approaches to work, etc would have never worked without having done FastAI.
For the most part though, I’ve been a very passive observer in most online forums that I take help from. I’ve contributed in some ways I believe to the FastAI forums, but I believe it’s not nearly enough. I have a habit of just chugging away through code on my laptop, figuring out everything myself solo, and then just forgetting about sharing that knowledge. I believe this habit is shared by a lot of other great developers too.
I like @Lankinen’s idea of using Notion. I have my personal website currently pointing to my page on Notion (akashpalrecha.me). I’m still figuring out what I’ll finally use to write more though. I’ve setup another website https://akashpalrecha.github.io (I found FastAI’s template on Jekyll’s website and happily adopted it 
) meanwhile. The issue with Notion is that sometimes it’s too slow to load up and the urls aren’t really that descriptive too. This may make it harder for search engines to find it.
I plan to write more about achieving very specific things in DL, like, maybe how to very quickly build a Resnet with a Mish activation without going through a lot of trouble, etc. In writing these posts, I will try and highlight methods you can use in general at other places. So far the only blog I’ve written sits on Matplotlib’s official blog : https://matplotlib.org/matplotblog/posts/an-inquiry-into-matplotlib-figures/
This was a long post!
Anyways, my semester plans have to change wildly now after taking all of this into consideration.
Thanks a lot for the opportunity!
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Hey everyone,
my name’s Oliver Müller. I studied applied mathematics. Programming was always a hobby of mine, I like the idea of implementing theoretical ideas and actually see them do something.
My start into machine learning was the book " Python Machine Learning - Second Edition" (Sebastian Raschka, Vahid Mirjalili). Python was already my go-to language, but this book introduced important libraries like numpy, matplotlib and the jupyter environment.
By chance I found the fastai course (2018) and was hooked. I remember the excitement of trying out those seemingly simple concepts to produce such astonishing results. Since then I followed fastai, completing both 2019 courses.
I’ve been told that I’m good at explaining complex subjects in an easy to understand way. As such I think I can contribute the best by writing summary articles about the lectures. Probably multiple posts per lesson, given their length and information density.
With that in mind: Is there any reason not to use medium? I know the paywall is frustrating. But as an author one can simply choose to not use their partner program and therefor not be listed behind the paywall, right?
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Hello Everyone,
My name is Ramesh Sampath. I work as ML Engineer and spend most of my time doing error analysis and collecting data where my model doesn’t do well. I come to FastAI for refugesince FastAI V1 days and very thankful for this community that Jeremy and Sylvian have built. Also had a chance to collaborate with few of my fellow FastAI learners in the past couple of years. Looking forward to reconnect with familiar faces and make new friends in FastAI Community.
@sampathweb on Twitter
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Hey folks, I’m Arkar.
I am currently based in Yangon, Myanmar. Currently I am working as an independent contractor for building ML/DL solutions. I and my colleagues started a deep learning startup at MIT delta V which provided training and testing visual data for evaluating visual perception systems of self-driving cars. Unfortunately, we had to shut it down when we ran out of funds. Before that, I was a Master’s student at WPI, doing research on using DL systems in education settings.
I love contributing my DL/ML knowledge back to community and I have been holding AI discussion groups here in Yangon to promote students and professionals to engage in technical discussions and feedback sessions related to topics in ML/DL.
I want to use this course as a platform for educating students here in Yangon. I want them to understand the source code of how fastAI is written (which I personally like) and encourage them to take things apart, debug and experiment them on their own to understand DL/ML systems via programmatic implementations (rather than inundated with a lot of math equations)
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Hello everyone!
I am an independent contractor helping small businesses set up their Data Analytics pipelines among a few other Data Science related stuff that they need help with. Contrary to most of the other attendees here, I’ hold a degree in Economics & I’m very interested in seeing the implications of Deep Learning in my field.
I like to keep self introductions short but if you’re willing you can check out my Medium profile, where I’ve recently started sharing updates and other information from my field. You can also follow me on Twitter to be instantly notified of when I share a post.
Looking forward to seeing what’s new in v4.
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It looks like the time of the year when our favourite little icons of the fast.ai family from all around the world become hyper-active on the forums and we get to talk a lot about Deep Learning & beyond
Hey Everyone,
I’m Sanyam Bhutani a fast.ai student since '18 and a fast.ai & Community taught ML Engineer.
I’ve graduated this year from a CS degree, if you’re curious, spoiler: it didn’t make me a better coder and I was fortunate enough to get a “dream job that I couldn’t have dreamed of” thanks to the awesome community and the course by Jeremy, Rachel and Sylvain
I’m really also hoping to take the course in-person by which I hope to bring the knowledge and experiences from the SF World onto the forums and back to my country too. My old classmates would remember “Making NNs Uncool again” from even then a very awesome course 
About what I do/aspire to do:
I try to Kaggle and I’m aiming at becoming a 3-Tier Kaggle Master this year (Moving close to it a little bit every day)
I also try to contribute to making the ML field a little uncool-er for the community by sharing 2 interviews per week with Practitioners, Kagglers and Researchers on a Podcast called:
Chai Time Data Science, Audio Link
I’m really excited to take Fastai v4 and continue becoming a better Kaggler/Coder/Blogger (In the shorter: 5-10 year) term and hopefully apply these ideas to challenges that could help the world at scale-agritech (Longer->10 year term)
During the course, I’ll work on 1 blog post per week based on the lecture and 1 based on lecture notes although I suspect it might end up being more than that number.
Really excited about the course and I look forward to meeting all of the fast.ai family members from all over the world on the forums.
Best Regards,
Sanyam Bhutani
Twitter: @bhutanisanyam1, Blog
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Hi everyone !
My name is Robin and I am a french image processing engineer (a young one, I started 3 months ago). I have been using fastai for over a year now, starting with the MOOCs on youtube, and later with the library when I started my internship. That is also when I started posting a lot on the forums, as many parts of the library felt very unnatural to me. So at first I mainly asked questions, but as I am an impatient man I started answering them myself by going indepth into the source code and testing the different functions to understand their behavior. Therefore I have been trying to answer as many questions as possible for some months, before having to focus a bit more on my work.
Lately I have not been using the library much, as I wanted to have a better grasp on what is happening behind the scenes when training. I considered starting using v2 for a moment, but it was still on early stages and I needed something quite robust for my job. So I transferred to pytorch lightning, which is good for many things and bad for others, but at least I am forced to make things myself. But this is the opportunity for me to get back to fastai2 now that it is more stable and complete, though I’ll probably finish my current project without it.
Speaking of which, I am currently working on a project that aims to grade breast cancers based on histological images of them to help doctors choose the right treatment. Since I started working on DL I wanted to make something that felt useful with it so I’m delighted to be working directly with a hospital to maybe help save some lives.
I am very grateful to be part of this wonderful community, and I’ll certainly be trying to get active on the forums again.
Best,
Robin
EDIT: I have also been a nbdev user since release and I am delighted with it, it is amazing how it helped my workflow ! Thanks also for this !
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I’m Vijay. I work in chat bot development and also occasionally on building data pipelines.
I read all the intros before mine and I’m in awe of the background of the people that’re taking the course. It felt like whole Earth for me. Also all the people here, if built a company, that would be incredible. Incredible in the sense of what all problems that company could actually work on. There’s geo-spatial analysis, health care, natural language, economics and what not.
Though I’ve been around the forums since the Part 1 2018 I was never able to complete the course to a full a extent, let it be the timings or the lack of motivation. For the past six moths, I had been following some good people and their work from the extended fast ai community on Twitter. I was astonished at what they’re achieving. Looking forward to stick to the course this year and may be inspire someone towards the completion.
P.S: Getting my new laptop this week, an added motivation for the course 
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Hello everyone!
I am Joan and I’m working as a Data Scientist in Hospital del Mar in Barcelona. Although my main work is related to other stuff I am pushing here to develop a DL based clinical related tools (with fastai!) in the day to day care. We are mainly focused in vision and histology slides concretely, but we plan to broad our spectrum when things start to get smoother.
My DL journey started a year or so ago and I had little experience in coding (and python) but I am struggling to improve my programming skills as fast as possible. Fast.ai v1 library help me a lot here since it was super easy to implement SOTA. Now I am playing with fastaiv2 (with @muellerzr course and help) and I find the update awesome!
As you can imagine, I am now very interested in the medical imaging module, deployment but also in importing PyTorch code to fastai. However I am really open to new and exciting projects in other DL areas!
Look forward to learning fastaiv2 and hope I could take most out of this course!
Cheers,
Joan
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Hi all!
I am Pablo, and my professional trajectory is a bit odd. I have a PhD in biophysics, and then made a postdoc in Copenhagen in neuroevolution (design of neural networks using evolutionary techniques, mainly an algorithm called NEAT) and also in mixed-initiative, where the design process is shared between humans and algorithms. After this I came back to Spain to join the company of a colleague. We have mostly done technological consultancy projects, and I specialize in NLP (I try, at least!).
I found Fastai in early 2018, when I was preparing to start working with Deep Learning. What really sets Fastai apart for me is the focus on top-level results from the first lessons and, much more importantly, the confidence that the Fastai team spends a lot of time testing all the many, many papers coming out in the field, and then incorporating (super quickly) what really works. The downside is that Fastai code itself moves very fast! I started using v0.7, then had to adapt to v1 and now we are moving our code to v2 (we are working on a devilish problem, so every update and little trick helps). So far, a reasonable price to pay
I also followed last year’s course, and I was very excited about part 2, where I gained a more solid understanding on how Fastai is built (really important if you want to change stuff!)
I would really like to think I am contributing to this community, although I am rather humbled by so many of you doing amazing work here and sharing great contributions. So I am specially thrilled to have been invited to follow the 2020 course!
Regards,
Pablo
EDIT: You can find me at twitter at @pablo_gps. Note that I post both in English and Spanish, though.
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Hi everyone, my name is Quan!
I am a Java programmer located near Washington DC though I have invested in data science and deep learning for nearly 3 years, starting with few MOOCs on Coursera and eventually learn about fastai MOOC from a friend. Besides projects in the course, I have used fastai v0.7 and v1.0+ to build some personal projects such as animated movie classification with GradCAM debugging and end-to-end deep learning for tabular and text data, collaborate with @Andreas_Daiminger
I plan to get involved in NLP heavily starting this year, working on both English and my native language Vietnamese (which was a goal of mine when I first started doing deep learning but never got the time to work on it). I have read some papers and currently doing Rachel’s NLP course. I plan to implement a few projects using the new version 2 of the library (I have heard a lot of good things about it!)
Overall, receiving Jeremy’s invite for the 2020 fastai course is a huge honor, since the most useful and practical knowledge about deep learning I have ever known comes from Jeremy’s courses. And another good news is that I am selected for USF’s Data Science Master program starting this Summer so I am extremely psyched to move to San Francisco and get to work with this year’s cohort and the fastai community there!
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I’m super-impressed by all the bios here of people doing great things with DL. To add some balance there is also room for people like me who are still exploring ML and DL without working in the field. I have been following on-line courses for the last couple of years and am now completing an in-person data science boot camp that I won in a Kaggle competition (thanks to fastai).
What would I like to get out of this course? I’d like to become comfortable working in v2 - I’m already trying out the tabular notebooks - and start doing original projects with a geospatial/environmental focus. Apart from DL, I am also a big fan of data viz.
Looking forward to the new course, although I’ll be on a plane during the first class, flying into Las Vegas. That’s near San Francisco, right?
Twitter: @Data_sigh
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Hi everyone, I’m Miguel. Currently I’m a PhD student of Geophysical Sciences, working mostly with satellite data and weather models/reanalysis data on applications related to forest fires. I recently published an article on a deep learning approach for mapping and dating burned areas using satellite images (https://authors.elsevier.com/a/1aN0a3I9x1YsQn). I’m a fastai user from the very start, I think I’ve watched all the versions of the course, or at least back to fastai v0.7. It’s always a good experience, there’s always something new to learn and the community here on the forums is amazing!
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Hi everyone. My name is Patrick and I am a PhD in Computer Science with core interest in mathematics and computer vision. I have been using fastai since the first day and I am still exited. Shortly, there will also be a paper where I used fastai to learn models with synthetically generated data. As soon as it is published I will report. I shortly started to look into fastai v2 and it looks really promising. A lot of progress has been made since v1. Also I attended all courses till now and are really looking forward to the next course and one more thing: thank you very much for the invitation.
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