My name is Matanya (@HananMatanya), I work as an NLP developer in a startup called DigitalOwl (and completing first degree in BSc). In the company, we do medical documents processing in the field of insurance.
I owe to the fast.ai community a lot!
The project with which I was accepted for work was Hebrew ULMFIT.
I created a learning group at my place of residence on fast.ai v3 part 1. The group completed the course successfully!
I think fast.ai v3 part 2 is one of the best practical courses on the web today in Deep Learning. A lot of my work environment is built on this course. It allows you to think and perform almost any experiment I want.
Anyone who deals with advances NLP with fast.ai is welcome to contact
To decentralize our experiments we use FastEc2.
I was very happy to receive the email and very excited about the course.
Bhoomit (@bhoomitt)
Work @ GoIbibo/MakeMyTrip, India
Building an internal Chatbot Platform
Using ULMFiT & fastai on production for intent classification for about a year
Learned most of what I know about NLP with V1. Looking forward to V4.
Hi,
I’m Butch Landingin, a software developer from the Philippines.
I first started my ML journey years ago with Andrew Ng’s ML course on Coursera, which in contrast to the fastai approach, uses a bottom up approach – starting with matrix algebra then building it up to understand more advanced concepts like forward propagation, back propagation and gradient descent .
While Andrew is great at explaining complicated mathematical concepts and making them easy to understand, it’s a hard course to master especially when you want to start applying deep learning to real world problems.
In contrast, when I first took the fastai course a year ago, I was very excited to be able to apply what I learned right away (and get good results as well!) just after the first few lessons.
I am excited to join this next iteration of the course and its improved fastai v2 library and would like to thank Jeremy, Rachel and Sylvain for building a fantastic course, a great library and an inclusive community!
Hi! My name is Andrea Panizza, I’m a Senior Staff Data Scientist at Baker Hughes and on Twitter I’m @unsorsodicorda. I got interested in Statistics years ago while working as an Aero Design Engineer for turbomachinery, then I started learning about Bayesian Inference, Statistical Learning…and finally in 2016 I got on the Deep Learning train As I’m mostly self-taught in these topics, I followed quite a few MOOCs in the past.
I wanted to start again by following the fast.ai MOOC and this time I tried to start a study group in my hometown, without success. So this time I’m going to do it the classic way, i.e., with you folks as my wonderful remote fellow students!
BTW, let me thank @jeremy and all the fast.ai people not only for this great library, but also for:
an excellent getting-started guide to GCP
finally convincing me to open my blog It’s still in beta, so to speak, but finally after a long procrastination, Jeremy’s excellent template for GitHub pages convinced me to start!
I work as software engineer since long time (like since ~2004) and was never involved in ML/DL in my day job role. Though, I’m enthusiastic about learning new stuff in general, and in ML in particular.
Last year I’ve enjoyed Swift for TensorFlow part of fast.ai course and even took some part in its development by creating SwiftCV library, contributing to S4TF itself, and doing a fun project with S4TF.
This year I suddenly for myself became PyTorch/OpenMined grant recipient for working on Federated Learning open-source project with PySyft team! PySyft is privacy-preserving deep learning framework based on PyTorch.
Privacy is a relatively new (yet important) thing in DL, and I’d love to hear more on it in the new 2020 part, @jeremy
My main takeaway from fast.ai is that you don’t have to have PhD to do things in ML. Thank you for that!
Feel free to connect with me in Github or Linkedin.
I am Ariel and live in Austin, Texas. I have been a huge fan of fast.ai and the people behind it, Rachel, Jeremy, Sylvain and all contributors. Not only because they are bringing all of us forward in this field, but also because they bring up important topics such as bias and how to avoid misusing AI.
I am a Software Engineer working on AI and have deployed text classifiers in production using Fast.ai. I have been lucky enough to attend the past few live classes remotely, and I am grateful for the opportunity to do it again in 2020.
Looking forward to learning from all of you and to continue my journey in this amazing and exciting field. I am also looking forward to the release of the Fast.ai book.
Hi! My name is Ravi Vijayakumar, I’m a IC Design Engineer at Broadcom and on Twitter I’m @hiphopswami. I got interested in Deep Learning 4 years back and have been a student of fastai from the beginning. I am still awe-struck at the forethought of Jeremy and Rachel in knowing the impact Deep Learning would have and the generosity of opening up all their experience to anyone willing to put in the hours and starting such a fantastic community. I have been putting in the hours but haven’t been consistent. I plan to throw the kitchen sink this time and look forward to participate actively in the forums.
Hi!
I’m Francesco, father of two and co-founder of two Data-related companies in Italy.
I have been following a couple of iterations of Fast.ai courses.
Together with my colleague @pietro.latorre we created the ULMFiT model for Italian (https://github.com/Quantyca/deepitalian) and some other prototypes!
Hi,
I’m a software engineer turned ML practitioner turned startup founder based in Bengaluru, India. I’m currently building Jovian.ml, a platform for sharing and collaborating on Jupyter notebooks.
I’m lucky to have attended the last 4 iterations of FastAI (Part 1 & 2) live online, and I recommend it to pretty much everyone I meet, as the best place to start learning ML . It’s a privilege to be part of the FastAI community. I look forward to organizing a FastAI study group in Bengaluru, as we do for every iteration of the course. I blog infrequently about FastAI, PyTorch & data science on Medium.
I’m James Briggs. I’m a data scientist and software engineer from the UK. I studied computational physics at university. I got a little interested in ML back in 2008 when i tried to read the
late David McKay’s Information Theory and learning algorithms. This book is beautiful, but it’s very math heavy. As a result it was hard to get inspired and see applications. I learned about basic neural nets like Hopfield nets at this time too. (Back then wikipedia said something like “neural networks have some limited applications, but they are largely a curiosity and likely won’t be part of the solution of AI.” Hehe)
I got into ML properly through Andrew Ng’s course in 2013. I tried to do things like cats vs dogs and galaxy zoo with classical methods, but gave up because the results sucked.
I struggled to learn deep learning before starting fastai last year. I’d been on a nvidia weekend course on dl and tensorflow, and at the end i felt that i would have to study for years to get good at this stuff. The course basically taught us some tensorflow and by the end we could code a cnn to solve mnist from scratch… yay!
Fastai was a revelation. I’d been learning things backwards all along! My old problem Cats vs dogs was solved the first day, it was even too easy. I immediately started using what i was learning at work, especially tabular learning. I even wrote up my own notes, on my blog, and taught my colleagues about what we could now do.
I’ve been fairly active on the forums, asking questions and helping people. I also made a small pull request to fastaiv1 a few weeks after starting, fixing a bug i found in how the Category datatype worked with python dictionaries. I also write a blog on github pages, where i have my personal lesson notes and project write ups.
Twitter: jimypbr
Blog: http://jimypbr.github.io/
I’d be very happy to see the early drafts - brian.smith@microsoft.com Jeremy. I’m not directly on the Azure ML/AI side, but we have some internal discussions started up.
I’m Nikky. I studied image processing and computer vision, focusing on image generation. I’ve gone through Part 1, v3, and part of Part 2, v2. I’m looking forward to doing more work with this version and improving my python skills. Especially writing more blogs in reimplementing interesting papers. One of my current favorites is Sin-gan, ICCV 2019 best paper.
I’m here for AGI I’ve heard it’s not coming very soon, so meanwhile I’d like to learn and have fun and contribute to the AI community. It would be great to join someone working on ARC.
My name is Elena and I’m from Germany. I just finished my studies in Computer Science with a focus on embedded systems. I have joined the fastai community around a year ago with no background on data science, machine learning or deep learning as during my studies, I specialised more in the microcontroller/technical computer science/electrical engineering area. I would also consider myself as one of those who have been told to “not being a math person” and therefore all mathematical fundamentals are always a challenge for me - I often have to reread things and think about those basics longer than others… Contrary, this was also one of the reasons I chose a challenging field of study (at least challenging for me ) - to convince myself that I can do those things, even if I may not be that fast in learning like others.
With the Pracitcal Deep Learning for Coders lesson videos I was introduced into the Deep Learning area and I was fascinated by this immediately. During my studies, I sometimes heard of those “magic/weird/complex neural net things” that always seemed to be too complex for me and therefore I never gave it a try. Watching all the lessons of v3 (and the v2 lessons as well) opened up my mind and led to a lot of understanding of the fundamentals. I really liked all those different areas where Deep Learning can be used and I started reading books, blogs and papers about it.
With the end of my studies nearby, in the middle of last year I decided that I wanted my first job to be something within the deep learning area instead of some embedded systems thing. Fastai definitely had a huge impact on this decision (and was the trigger for changing my field of studies) and is the reason I started learning so much about deep learning. So now it is 2020 and I have just completed my first month in my new job where I will be doing some research in the field of neural net robustness and safety. I am excited for this new journey and am looking forward to gather all the knowledge out there on deep learning - with the new course being one of those resources !
As I am learning best when I write down all the things I hear/read/see, I will try to take the time to write down my notes on the new lessons and share them here!
I don’t have a blog or active Twitter account yet, but I think I will give it a try this year
Hello Everyone,
I’m Abhinav from Mumbai , India. I’ve been studying the fast.ai lessons since 2019. I’m a software engineer turned Deep Learning Practitioner.Last years part 2 was just amazing , it helped me get a job at a startup in Mumbai .I’m currently working at a startup in Mumbai where our main focus is on misinformation. Fast.ai has really helped give me a huge leg up in this domain. I’m excited for v2 , for learning with everyone and extremely excited to see how I can apply fastai v2 at my job.
As I’m mostly self-taught in these topics, I followed quite a few MOOCs in the past.
My name is Vova, I’m from St Petersburg, Russia.
. It’s a privilege to be part of the FastAI community. I look forward to organizing a FastAI study group in Bengaluru, as we do for every iteration of the course. I blog infrequently about FastAI, PyTorch & data science on 
I’ve heard it’s not coming very soon, so meanwhile I’d like to learn and have fun and contribute to the AI community. It would be great to join someone working on 
) - to convince myself that I can do those things, even if I may not be that fast in learning like others.