Hi everyone, I’m Matanya.
I’ve been a fan of fast.ai since 2019, so I also ran a study group for the first course.
Today I am the data science team leader at DigitalOwl. We summarize medical files for insurance companies, where the information we process is both unstructured (ie PDF files) and electronic information.
In my opinion, the 2019 part 2 course is one of the best courses I’ve ever done. The perfect “toolbox” for a Deep Learning developer. so I’m really looking forward to its new version!
Good luck to everyone!
linkedin
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Hi! I am Omer from Israel, working on ML in the medical domain.
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Hello everybody (in Jeremy’s voice), my name is Dahir Ibrahim.
I’m a computer engineering student in my penultimate year in college. I’m a YouTube + other free resources taught programmer with 5 years of programming experience. I love the idea of bringing AI to everyday electronics which is why I’m working on building something I call “Deedax Inc”.
Deep learning for coders is actually the first thing that got me out of the infamous ‘tutorial hell’, so thank you and I’m honored to be here once again for new experiences.
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Hi, folks!
Great to be back here. I’m Alexis Gallagher, an alumnus of past FastAI courses, and helped out a bit with the FastAI book. I’m working at Google on ML, where my work doesn’t involve AI art at the moment, so I’m very curious to dive in!
Alexis
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Hello all,
I’m Maureen Metzger, from Ann Arbor, Michigan, USA. I’m so appreciative of the opportunity to participate in this course!
I stumbled across Fast.ai in 2017 after completing a Python sequence and a Machine Learning specialization on Coursera. I completed part 1 and some of part 2, and found the biggest challenge was my limited Python skills. They’re much better now after 4 years heading research and analytics for a healthtech startup. I haven’t gotten much opportunity to use deep learning in my work so far, though. I’m hoping to rectify that by updating my knowledge and skill set.
My primary interest is NLP. I am particularly interested in natural language generation of descriptions and interpretations of tabular data and images of graphs. I’m sure that sounds boring to some, but it’s very practical 
I sometimes feel odd as a middle-age female in tech, still up-skilling and trying to learn new things. But I’ve found my happy place – better late than never!
Cheers to all, and happy learning!
Twitter: @memetzgz
LinkedIn: maureen-metzger
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Hi Robert, catching up with forum. Yes, happy to connect @gamino on twitter or https://www.linkedin.com/in/gamino/
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That doesn’t sound boring—one of my main datasets at work is part graph, part tabular data — sounds interesting!
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Hello All,
I’m Jithin from Sydney, Australia. I am stumbled upon Jeremy’s course in 2019 and really liked the way Deep learning was taught. A big fan of Jeremy’s work. I currently work as a data scientist for a commercial Real-estate giant at Sydney focusing on personalisation models. Very keen to delve deeper into part 2 of the course.
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Hi everyone!
I’m Zac. I’m a physicist turned data scientist turned computational biologist. My latest projects have been around creating generative models for protein design and evolution. Diffusion models offer an exciting new approach to some of the problems that I’m interested in. I can’t wait to dive into understanding how they work! Many thanks to Jeremy and the team for hosting this course!
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Hey Michael, super to hear that you’ve been involved in starting OpenBioML, I saw it under Stability’s org tree somewhere recently, probably on their site. I was thinking of checking back on it sometime, but been a bit busy. I’ll be pinging you this next week, would love to be a part of the open org and contribute. 
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Wow oh my gosh this is a diverse group!!! (Just speed read through 300+ intros )
Hello all! My name is Michael!
I am currently living in Los Angeles (@appliedml42 I saw you were here too,) taking care of my mom, and attending the Recurse Center programming retreat online.
I decided to do Recurse because I am deeply intrigued by image generation as a creative tool. I just finished a project at Recurse making a deck of tarot cards using an instance of Stable Diffusion that could recognize “Michael Nagle” and make tarot cards with my likeness in them. I’ll include one here.
I have been blown away by the (Cambrian?) explosion of image models, and papers about image generation, and Twitter and Discords showcasing DIY implementations of said papers. It’s really astonishing to me. I tend to greatly prefer learning by finding my own way, making things, following my own instincts, so I was quite on the fence about signing up for this course, and then in hitting a bug in the repo I was working with that implemented a version of the Dreambooth paper, it hit me just how tenuous my understanding was of how Stable Diffusion really works, let alone feeling confident enough to extend it on my own, and I figured I could use all the support in deeply understanding it.
This is my first fast.ai course. I had a formal background in pure math in college ~17 (!) years ago, and so am figuring I will be able to fill things in as needed. I’m not working right now. I was last leading product research at a startup called Coda, and then lost my dad to COVID at the start of the pandemic and have been on a long sabbatical since, and am really trying to find my way back to work through following my own creative interests. So, this is an unexpected stop on that journey.
It feels like a lot to jump into and I am hopeful it will be thoroughly rewarding experience. Nice to meet you all!
The Stable Diffusion + “Dreambooth” major arcana deck that I made of tarot cards based on Michael Nagle lives here for now, for anyone curious to see: Nagle Arcana
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Good day all,
My name is Jacques, you can call me Jack.
I work in the rail industry in Melbourne, Australia.
Just finished the fastai unconference which was a blast! Had a lot of very constructive and life changing conversations. I’m rejuvenated and raring to go go go!
Will be playing around with some projects which I’m excited to share with and work with you.
To everybody that’s new welcome! This community is amazing both in brains and heart. Looking forward to seeing everyone’s works on the forums.
Please don’t forget to respect each other and continue to share kindness to the world.
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Hi everyone!
I’m Meng from Taiwan. Now working as a MLE @ SmartNews in Tokyo. My daily work are mostly about recommender system and NLP tasks but I’m so excited about the possibilities of image generation application that recently I have built a Slack bot for my colleague to play with Stable Diffusion.
I also use dreambooth to finetune the SD to recognize my company’s mascot: chikyukun
(which means “earth” in Japanese) 
Now I’m so happy to be here to learn with everyone and have a deeper understanding about the technology. Let me know if you want to collaborate on projects or want to hang out in Tokyo.
Thank you for reading and hope we learn a lot with jeremy! 
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Hey all,
I’m Brad, I’m based in Perth, Western Australia. I work in ML-based remote sensing and ML in agriculture. I work almost exclusively on computer vision problems, and have an interest in weak/semi/un-supervised learning stemming from the amount of time I and others have had to spend annotating data. It seams gathering data is the easy bit, labelling it is what requires work.
I’m also a PhD student at Curtin University, where I work on largely the same areas of research.
I’ve dropped in and out of the fast.ai courses over the years. I’ve finished part 1 at least once, and I finished the last part 2 course. Although I make relatively little use of the library (I tend to stick to detection-focussed libs or pure pytorch), I try and learn what I can from the course each time.
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Hiya,
I’ve worked as an ML engineer for computer vision in agriculture, and a software engineer building tools for ML engineers. I’m taking time to go through fastai so I can work in plant breeding or organism engineering. There seems to be a lot to do yet… I’m doing this fastai v2 because I’d like to be able to read then implement research papers then adapt them to different data types like regulatory DNA, proteins, or biosynthetic pathways.
After this course I’ll be hanging out in the open bio ML channels as well!
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Hi all!
I’m Ali and I’m based in Tokyo, working as a data scientist at Octopus Energy (shout if you want a referral code for ¥5000 yen off your electric!). In terms of interests in ML at work, its pretty varied - NLP for customer focussed stuff, a lot of forecasting work, and some more exotic stuff to do with smart grids and battery optimisation etc.
I did the fastai part 1 course back in 2017 when I made the switch from developer to data science, and was a huge fan. I’ve always been interested in going a bit deeper - I studied maths at uni so I quite like getting into that side of things, so I’m super excited for part 2 (especially since I’m now on a sociable timezone!) I rushed through the up to date part 1 again over the last few weeks to get back up to scratch.
Unfortunately I was on holiday last week so missed part 1 live, but excited to follow along live next week! Good luck all!
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I’m in Toronto too. Not sure I can make too many of the 4am starts myself!
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I’m based in Toronto working on training healthcare workers in VR using game engines (Unreal Engine) and AI (conversational ai).
Besides utilizing existing Azure and nVidia APIs for NLP I’m personally very interested in neural graphics and happy to be learning more about Stable Diffusion - hopefully on the road to generative 3D assets like the Nvidia GET3D paper.
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This is so cool! I made a stylegan-2-ada dataset of yuya-chara, I should learn to finetune stable diffusion
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