Hi everyone! My name is Ugaso (you-ga-so) and I’m a Machine learning engineer in the entertainment industry. I was lucky enough to pivot into this entry level role from software engineering, from doing work on my own tinkering with GANs to make generative art with stylegan-2-ada / vqgan. I’ve been following fast.ai for a while, and taken part of part 1 & 2, intro to ethics in ai (highly recommended!) Very excited to relearn the foundations, as someone who has a firm background in software eng, and get a deep dive into the popular generative model Stable Diffusion – particularly the hairy math parts. I won’t be able to join live but if you are located in Pacific time zone and would like to study together, please reach out.
Please connect with me on linked in if you are interested in generative art, interactive media, and applications of ai/ml to build and enhance artist tools https://www.linkedin.com/in/ugaso/
Hi, I am Jens Beyer. I got my own small company (lavrio.solutions) with my wife advising companies in Germany on AI and ML since 2016. We are building ML systems, providing guidance and trainings what to expect from ML/AI. We are mostly dealing with tabular or text data (sales forecasts, government forms, medical texts etc.)
The whole “creative” part of AI is something I can learn so much more about, so I am very excited about this course. Part 1 was superb, so let’s get to part 2
I’m super excited to start part 2 of this course! I live near Asheville, North Carolina in the USA. I have completed the first Fast.AI course, HuggingFace RL, several Nvidia 1 day classes and FSDL22 but do not have any work experience yet in ML. My past work experience has been
in records management software consulting for law firms and corporate legal departments.
My name is Romain, I’m from Lyon, France. I studied history and social sciences as a first curriculum, I worked and taught at university for some time (mainly social sciences things, but also technical topics mainly linked with stats and some Python) but now I’m back to some more studies because I’m looking to be a developer and I’d love to work as an NLP practitioner (I’m kind of fascinated by languages).
I followed fast.ai course 2019 (Part 1 & Part 2), completed it, did not understand that much the first time (and forgot about the rest that I understood), I followed almost all the lessons of fast.ai 2021 and read a big part of the Fastbook, and I hope that now it makes more sense to me
I’m really excited for fast.ai 2022 part 2, I hope we learn tons of things together.
My apologies for the late introduction! I’m thrilled to be able this year to attend the streaming as I’ve been carefully following all fast.ai lectures since their first iteration
My name is FG (short for François-Guillaume ), and my background mostly revolves around computer science and engineering! I’ve been working in early-stage startups for the last 5 years, specializing in deep learning (computer vision mostly, but NLP & RL as well) and doing open source
Here are some projects
I’ve contributed to: PyTorch (especially TorchVision), TensorFlow
I’m proud I started: Pyronear (wildfire detection using deep learning on edge devices), TorchCAM (DL interpretability for your vision models), docTR (high-quality OCR for PyTorch & TensorFlow)
So, in order to stay up-to-date, I’m trying to check the yearly versions of the FastAI course! It’s been very rewarding so far, so I’m glad & thankful I was invited here
If you want to connect, catch me on GitHub, Twitter or LinkedIn
Looking forward to everything we’ll build in the next weeks!
Hey,
I am Steven from Virginia in the states. I was a mechanical engineer until a coworker showed me fastai back in 2017. I got hook and switch to data science at a manufacturing facility (and now a machine learning engineer for the whole company). I used fastai to win the company’s innovation fair last year by making a segmentation model for defect detection. I also went back to school at the start of the pandemic and completed a master’s in robotics engineer this past May. My capstone was to train a drone in simulation to map damaged roads after a hurricane to assist first responders. The system was reinforcement learning based but in true fastai style I initialized the agent with a pretrained ResNet.
I am really excited about this course because I haven’t shut up about stable diffusion since I saw the first DALL-E-2 results.
Hi, my name is Nimit. I am a machine learning engineer. I love exploring new and fun ideas in machine learning and information retrieval. I have learned a lot from whom I follow on Twitter (my handle is @nimitpattanasri) and have enjoyed sharing what I learn with my colleagues.
Just to confess that I was late to the game, but managed to binge watch Practical Deep Learning for Coders 2022 (adjusting the speed to 1.25-1.5x and turning closed captions on works well for me). I like your teaching style and enjoy learning from you. Love your tips for productivity hacks at the end of the course. Jeremy, you are an awesome and inspirational teacher!
In my spare time, I focus on foundations that I failed to catch up with. I love it when all the dots are connected. And I am sold by this course’s title
Inspired by recent NVIDIA and Yann’s talks about autonomous systems, here’s a list I’m perusing:
Justin’s DL course and Alfredo & Yann’s DL course. Joint embedding models seem exciting and predicting an embedding of the target (instead of the target itself) makes a lot of sense.
Hi everyone! I’m Manuel Araoz, Argentine living in Uruguay . My work is mostly related to blockchain security, but I’ve been a fan of AI since… I have a memory. I studied some AI in college but I graduated in 2012, so most I know is pre-AlexNet and deep learning. Marvelled at the latest image and text generation results, I’ve recently decided I wanted to understand how DALLE2, MidJourney and StableDiffusion work in depth (as in: I’m able to build my own). Googling around I found this course, which is more than I could hope for. I managed to sneak in this late thanks to my open source contributions to OpenZeppelin. Looking forward to catching up with the course so I can follow along live! Nice to meet you all and thanks for the opportunity!
Hello, all. What a fabulous group. I hope we have opportunities to chat and collaborate.
My name is Charlie Deck. I’m an American living in London. I mostly run around corralling my three children under six years old. Sometimes I do manage to go to the office where I work as a design lead and engineer for DeepMind. You can read a bit about some of the papers I worked on at http://charliedeck.com, along with some (rough) projects from my pre-DeepMind days.
I’m keen to get my hands dirty and dig into the details of deep learning – I often find myself working on technologies adjacent to the actual DNN stack. I love being comfortable enough with a technology that I can feel expressive and creative with it, and I have an inkling that the things we’ll be discussing have tremendous potential for creative re-purposing. Exciting times!
I’m “bigblueboo” on twitter and linkedin and whatnot.
Hello everyone, I am Abhilash from Bangalore, India. I have been working as an embedded software engineer for 5 years now and always wondered what ML/AI has been about. Started the DL course part 1 a few weeks back and am completely hooked.
Looking forward to learning and building together.
I’m Carson and am a professional embedded software engineer with experience of deploying models on embedded systems. I initially took the v1 course back in College but am excited to see whats changed in the DL space since then. If anyone wants to talk about embedded ML, let me know!
I’m based in the Washington DC area, and am very happy to join this course. Through it I discovered that it is the most productive time to get up before 4 am and working until my kids start waking up around 6-7am.
I’m a government employee in the regulatory space for AI/ML-enabled medical devices/software. I hope this course will help me be ready when similar novel models/methods start to appear in commercial products in the medical domain, and I’m sure that it will be very helpful in my own academic research.
Hi everyone,
I’m Sylvain. Joining this course a little late but hope to catchup soon! I currently work on speech recognition & speech synthesis as a ML engineer. I’m also a musician with a passion for music production & music technology. Very excited to understand and explore the creative possibilities of stable diffusion models! https://twitter.com/slegroux
I’m Laurent, I live in France (Strasbourg), I am the cofounder and current leader of team of 250 people whose mission is to deploy AI solutions wherever it makes sense in a large banking and insurance group.
I’ve been following fast.ai courses since 2017, and I got a scholarship for this fantastic part 2 of the 2022 edition by contributing the following bits to the fast.ai community:
A french version of the previous version of the course (20 hours of youtube videos + notebooks in french): https://www.cognitivefactory.fr/
I’m mostly working on Natural Language Processing (conversational assistants, email analyzers, user feedback analysis), Document Images (images improvement, pages splitting and classification, OCR information extraction), and Speech to Text (voice dictation, IVR in natural language).
I have been taking fastai courses since 2018, back then I just made the decision to transition from my Bachelors in industrial engineering over to computer science for my masters. Previously got hooked by the crypto space in 2016 and spend some time learning javascript for a few side projects (trading, running crypto pools etc.). Then I stumpled upon some deep learning videos on youtube and was fascinated and still am by the endless possibilities of training powerful algorithms with a few lines of code. This current part 2 with stable diffusion just seems to light the same fire again as when i first learned about CNNs or reinforcement learning for Nintendo games:)
After graduation i worked in applied NLP (mostly scraping → transforming → clustering data for clients). I am currently a data scientist in the automotive industry working on pricing for the used car market, so back to tabular right now
Me participating on part 2 is part passion for the technology, part just trying to still be part of everything fastai does because it taught me so much and I would not have made the transition from industrial engineering to data science without it, part sharpening pytorch skills again (I am mainly working with catboost atm) and also just having fun and finding a new exciting side project. And of course also connecting with amazing people who share the same passion for fastai & ML/DL in general. Who knows what will come from that
Oh and I also like studying Mandarin in my free time!
Wishing everyone a great time! Best wishes from Hamburg, Germany
https://www.linkedin.com/in/ugaso/
), and my background mostly revolves around computer science and engineering! I’ve been working in early-stage startups for the last 5 years, specializing in deep learning (computer vision mostly, but NLP & RL as well) and doing open source 
living in Uruguay 
. My work is mostly related to blockchain security, but I’ve been a fan of AI since… I have a memory. I studied some AI in college but I graduated in 2012, so most I know is pre-AlexNet and deep learning. Marvelled at the latest image and text generation results, I’ve recently decided I wanted to understand how DALLE2, MidJourney and StableDiffusion work in depth (as in: I’m able to build my own). Googling around I found this course, which is more than I could hope for. I managed to sneak in this late thanks to my open source contributions to 
nice to be a student from time to time