I am Chansung from South Korea, and I am super excited for the part2 of the fast.ai course. I have a couple of personal relationship with fast.ai that I have translated the machine learning video lectures and the fastai book into Korean, and along the way I
have felt that fastai is the right place for a SWE to get involved with DL world just like myself.
Currently, I am running a couple of side projects about MLOps as a ML Google Developers Expert. It has been quite a while since I took fast.ai course for the first time, but I am 100% sure that I will learn lots of lessons again! and I am looking forward how I can adapt/fuse them with what I know these days. Possible, it would be fun to showcase MLOps with fastai
Hi everyone!
I’m Manikya and I am currently working as a Data Scientist at Zscaler, mostly doing unsupervised stuff in Cloud Security. I was also a contributor to FastAI.jl (a julia library which takes inspiration from fastai), and worked on the tabular pipeline for the project.
The Fastai course (v3) is what got started me in my machine learning journey and am really looking forward to learning more from part 2!
I’m not very active on social media, but here’s my github and LinkedIn.
fast.ai really opened my eyes to the world of DS/ML when I did the first course back in 2018 I think? (The first one on pytorch). I’ve been able to put together a small team within my dept focussing on solving real problems in the video broadcast and distribution space. Also, occasional contributor and honorary member of core-dev at IceVision. LinkedIn, Blog
Hi everyone, it is great to be here. Thanks for the opportunity, Jeremy, I’m honored.
I was already digging the previous iteration of the second part and was amazed by the content not only for the ML but also in the programming and engineering part. In short, I’m incredibly excited about the second part of the journey.
A tiny reminder of myself, I’m currently working as a Tech Sales Specialist at Autodesk, my background is Architecture, and my goal is to find a sustainable way to spend all my energy learning and practicing ML.
I have started my deep learning journey years ago by taking one of the first versions of fast.ai while getting a degree in economics in China. After that course, I decided to pivot into software engineering and AI/ML. Since then I changed my career path. I have worked with deep learning in medical data, computer vision, robotics, and genomics data. Currently, I work at one of the big tech companies working in supporting AI/ML products and infrastructure at scale.
The email from Jeremy a couple of days ago that mentioned that I got the scholarship to attend this version of the course live came very unexpectedly and made my day.
I feel super excited to be able to attend the latest iteration of fast.ai v2 and learning about stable diffusion!
I’m Carol Willing. I’m active in open source, in particular, the project leadership and development of Python , Jupyter, and mybinder.org. I’m a long-time fan of the work that Jeremy, Rachel, and Hamel have done in the fastai ecosystem .
I’m passionate about making learning and education accessible for all. I’m looking forward to seeing this course evolve and am honored to be taking part in it.
When I’m not doing open source stuff, I am VP of Engineering of Noteable.
Hi everyone, I’m very excited to be here, and to see a bunch of familiar names too.
I started on ML around 2016/2017, and I learned a lot of my software skills hand in hand with fast ai. FastAI’s been the source of a lot of interesting work, whether through partnering (DAWNBench, SwiftAI) or on its own (NBDev) – so I’m really looking forward to where this focus on stable diffusion will lead.
I work on AI in aerospace and robotics, you can find me on LinkedIn or Twitter.
Hi all! This was a blast from the past in my email and fast.ai was one of the first steps in my journey years ago. I’m currently studying Data Science for Public Policy at Georgetown in Washington, DC. These appear to be happening at 4am my time, so I will be watching these videos and keeping up with the content as best I can “from the past” while you guys lead us into the future.
Hi All !!! It has been a great learning path since I took this course back in 2017 on my Bachelor’s day, After 5 years of working as a data scientist still this course teaches me a lot about the latest research and development in the Deep learning field. In my work, I mostly worked on product recommendation, campaign generation, and building chatbots. In my free time, I like to learn more and more about deep learning research, especially from the fast.ai courses.
Looking forward to getting the most out of this course and from my fellow peers.
My name is Aayush Agrawal. I have been following fastai from its inception course and never missed taking any iteration of this course. I work at Microsoft as a Senior data scientist and I don’t typically do deep learning in my current job. This course help me keep up with all the latest developments which are happening in the field and is by far the most useful course for applied scientist like me. Here are few ways you can connect with me -
The coolest project I have done with FastAI is Orca Hello which is an AI-assisted hydrophone monitoring and alerting system used by researchers and conservationists to protect SKRWs. It basically listens to underwater hydrophones and try to predict if there are Orca calls in it, this helps in early detection and conservation activities. You can read about it here - OrcaHello live inference system – AI 4 Orcas and live website is here - OrcaHello (aifororcas.azurewebsites.net) where you can listen to detected Orca calls. If you want to get some system overview - Here is a youtube link for a seminar we presented this project.
Looking forward to learning from the course and fellow peers.
Based in San Francisco, have a regular day job at Google, leading global product go to market strategy for various ads products.
Don’t have a tech/CS background but have dabbled in various 101 CS/AI/ML courses incl. the Part 1 DL course in 2020.
One of my goals back then was to build a bit of a network of DL enthusiasts for mutual inspiration and fun. Obviously the pandemic made that a bit harder in large part because the course ended up being virtual vs in-person. But I really enjoyed it and met new people through the study groups.
Other interests: Technology/AI/ML/DL, Music/composition/production/technology, Art/painting, Architecture, Movies, Sustainable food & fashion, Investing in tech startups, Mediation/yoga.
Weekly in-person meet up in San Francisco?
With Jeremy based in AU and hosting the course from there this year, I’m curious to see if local folks in/around San Francisco are interested to join a weekly meet-up to watch the classes in-person together (at a San Francisco friendly time, e.g. Tue 6pm PT).
If so, maybe we can find a classroom style location somewhere and perhaps one or more in-person teaching assistant(s).
@jeremy would you by chance have relationships with USF that we might leverage to use a classroom? I’m happy to follow up/work with them but would appreciate an intro . Also, if you’re aware of any potential teaching assistants based in/around San Francisco who might be interested to help out, please let me know
Hi all! I’m Christine, I took the fast.ai course back in around 2017 & learned so much from it, really happy to come back again now, especially with so much new material! I’m working these days at OpenAI (at first I was doing music generation research and now I’m managing the team that just published Whisper, a speech recognition model).
do you have a sense of how many SF people might attend? if USF isn’t easy to arrange, I could ask if we can do this at the openai office, tho I have no idea how likely it is they’ll say yes)
Hi Christine, that would be great! I have no idea how many SF people are interested to attend in person but will hopefully have an idea soon as I’m about to publish an interest form [edit: linked here]. Will keep you posted and thanks so much for offering to ask!
I spend most of my time building open source software for data exploration, analysis and publication: https://datasette.io and related tools. But more recently I’ve been writing a bunch about AI and ML stuff:
I’ve also been exploring an interesting security attack against software written on top of large language models such as GPT3, called prompt injection:
I’m particularly interested in understanding how these models work well enough to explain them to other people. Very excited to learn that as part of this course!
This sounds great… There were some of us (in 2017/18 at least) from the south bay as well, we used to brave the traffic for our weekly dose of fastai. I’ll keep track of your other thread @steef.
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