Good to see you here Ramesh 
good concept:)
Hi. I’m David. Working as a MD in the Netherlands.
A TED talk by Jeremy from 2015 motivated me to do a MIT MOOC on programming.
Got distracted since by doing a Master in Epidemiology, work, life, internet 
and other stuff…
Currently I’m busy with the first three lessons DL1 2019 and practice on lympf node pathology slides.
I’m curious about applications for health care. I’d like to think more can be done on prevention. Health Care absorbs >10% of GDP in the US. A concrete subject I’d like to examine would be gait analysis/speech analysis and neurodegenerative diseases.
Machine learning could be used to stimulate healthy behavior: diminish anxiety, stress, poor sleep, addictions etcetera. There are already projects running in this field, for example chat bots and apps helping people with diabetes.
What I find difficult is to not get distracted and keep my head clear while spending time behind the computer. Sometimes I wonder how good an investment coding is. It changes rapidly and I worry a bit about automation. Jeremy gave an interesting talk on the future of coding. Happy though if something works!!
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Hi everyone, I am Abhilash. I am a Business Analyst in Bangalore, I work in Computer Vision, but I am no way any kind of expert yet. I am really having a hard time figuring out the steps to take for my career. I want to be part of applied research where I not only research but also implement them which can bring change to the world.
I am not new to code, but I am struggling to get out of that intermediate zone, where I know stuff but very little practical experience. I have taken a resolution to complete this course will full sincerity.
Anyone who wants to connect online with some project, or any suggestion, advice always welcome.
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Thanks Kaspar. Please feel free to give it a try and share your feedback on the product.
Here’s the link to the GitHub repo: https://github.com/ModelChimp/modelchimp
Hi all!
I’m Josh, I’m an astronomer by trade, but excited to play with Machine Learning. I’ve tried a few courses before, but they’ve been quite theory dense.
Got through lesson one and during a conversation with a friend, decided to have a very stupid classification which could tell the difference between bees and horses.
Stupid I know, but after ~500 images downloaded from google it achieved a 100% ACCURACY!
These were the only images it was even remotely unsure of. Now I know this isn’t a surprise, I would be concerned with anyone who couldn’t tell the difference between a bee and a horse in any image =p But was very impressed how easy this was to make. Excited to move onto lesson two!
Josh
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Hey guys, Im Aditya, an undergraduate from India. I have taken a few MOOCs on coursera before this. This is my first try at FAST.AI. Hope to learn a lot from the course and the community. I’m searching for ideas to do the Lesson-1 assignment. Wondering what dataset i must use to train the classifier. Anybody with suggestions?
Also, would like to form a small group consisting of fast.ai students from across the globe so that we can share our ideas and also our take-aways after each lesson. Please do ping if anybody is interested.
You can read my blogs at https://medium.com/@adityaananthram
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Hey all, I’m Florent a french CS student, so happy to started learning ML with FastAI =D
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Hi Everyone,
I’m Nick. I have my PhD in Industrial Psychology and have gotten into ML and DL over the past few years. I was reading books and taking MOOCs on using PyTorch and noticed fastai as a Keras like wrapper for PyTorch option. Once I saw the class was free and what cool stuff Jeremy was doing I had to join. I grew up playing Inline hockey, so for my first one I decided to train a classifier to see if it could recognize the difference between inline hockey and ice hockey. Just for a fun 3rd class I added lacrosse. It ended up being 96% accurate on a test set of ~45 images from each class. Pretty impressive.
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Hello, Frank here, also from Kenya, student at egerton actually, I’m hoping to get a job in deep learning in a year actually
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Hi Everyone! I’m Kathryn from Boston, MA - currently a data scientist working in biotech. I’m interested in applying deep learning at work, to various aspects of drug discovery.
I have worked on deep learning projects in the past (usually with tensorflow), but found it quite difficult to understand how to optimize parameters, understand model failures and ultimately make it useful enough to justify spending continued time and resources pursuing these projects. Hoping I can change that with the help of this course!
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Hi Walter - nice to see another bostonian. 
Computational neuroscience is fascinating! We recently had a neuroscientist from the Broad/Qstate biosciences speak at my company about computational techniques (among others). One of the most interesting talks I have seen in a while. good luck with the course!
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Hi all,
I am from the beautiful island of Tahiti 
I am an analyst and like my peers in the industry, Excel and PowerBI are my main tools. But less than a year ago, I was given a project where these tools showed their limitations. So I started looking for solutions elsewhere and discovered Programming and Python 
.
After almost 10 months, I have learned about Pandas/Matplotlib, automated a lot of my work thanks to these libraries and have grown passionate about Data Science, ML and DL. And here I am now, happy to join the Fastai community and really enjoying the experience. 
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Hi all!
I am from Genova, Italy - you may have heard of Portofino or Cinque Terre. Genova is the city close to those marvels, with an important harbor in the Mediterranean and an history of industry and innovation.
I am running an AI and ML meetup here, and I am going to run a study group on fast.ai deep learning v3.
I am struggling with the selection of the platform to use for the meetup, so I am following the lessons and running the conde on Kaggle, Colabs as free options and on Gradient as paid option. I am taking notes of the experience here
should anybody have comments or suggestions, you are very welcome!
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Hi, I am Abdu Issa from Houston, Texas. I work in oil and gas and recently got into deep learning. I started a couple of months ago with part 1 (2018) and then part 1 (2019) came out when I was on lesson 4, so I decided to start the course over since the library changed. I like the 2019 way of doing things, so I am running with it.
One interesting thing I have been trying is going 100% iPad Pro— that is to say I have been using a 2018 iPad Pro as my only device for all of my programming, machine learning, and deep learning. This has been made possible by a bunch of cool apps and cloud services. So far, so good—I haven’t run into a problem I could not solve on my iPad. I wrote a medium post about my journey here: https://link.medium.com/I88XlxTU0T.
My goal is to be able to apply deep learning to poetry, song lyric generation, and also find applications for the oil & gas industry (and add value to my employer). Oil & gas is a quiet industry in terms of deep learning so far, but I hope to change that.
Looking forward to working with and learning from everyone in this community!
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Hi, I’m Ben from Florida. I’m an actuary so I have quite a bit of domain knowledge in insurance. Deep learning isn’t prominent in the profession due to the strict regulation and the belief that linear models are the only predictive models that can be explained with sufficient detail to receive regulatory approval.
I’ve got a light programming background in R and SQL and experience applying GBM (xgboost) and penalized regression (glmnet) models to predict insurance losses using tabular data.
I look forward to learning python and exploring potential deep learning applications in the actuarial profession.
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Hi, I’m Ronnie Quiñonez. I’m a undergrad student looking to do ML research. I’m based out of Southern California.
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Hi, I’m Josh and and I’m an Electrical Engineer by education, but have been working as a Data Scientist for the last 3 years. I’ve been wanting to explore more opportunities to use deep learning in my work, and fastai seems like a great way to get started. For the lesson 1 homework created a classifier that predicts ribs, brisket, steak, and hamburgers. I got a smoker a few months ago, but the cold Michigan winter has prevented me from using it recently 
I downloaded about 1000 photos from Google ages with the Firefox tool. I’m only at about 82% accurate right now, but I really haven’t done any “cleaning” of the images and I’m pretty sure there is some mislabeled data. Im
I’m really excited for the course, and I’m hoping to get into regression (not just classification) of images and text as it would be quite relevant to my work. I’m also doing the course on my Windows laptop with a mobile P2000. it has been slightly slower than the benchmarks so far, but I will try and ride it through as much of the class as I can!
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Hi, I’m Ricky, a Cameroonian who started AI with Deeplearning.ai by Andrew Ng. After that I decided to do fast.ai course to complete that one and I started with the machine learning part. This week I decided to start the deeplearning part and advance with the 2 at the same time. Also took some other MOOCs on coursera, udemy and Udacity. After this I intend to apply the knowledge on some problems here in my country, and since I can’t get internship here because it’s not really known here I will also practice on kaggle to gain some experience.
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Hi everyone,
I am Bilal from Algeria. I’m a PhD student in computer science. I’m very interrested on ML, DL and speech recognition. I’m so happy to be a part of this community. I’m looking for improving my knowledge. learn from other examples like ML.
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