Hello everyone 
I am from Mumbai, India. I first experimented with machine learning early last year. Since then I have done the CNN course by stanford and a couple of machine learning courses online. Have relatively less hands-on experience with deep learning, just one-two small projects. Got to know about fast.ai very recently. Looking forward to doing exciting projects in this course!
And I am super intrigued by GANs and style transfer! (Hola @helena!)
Hello everybody
I am from Bangalore, India. I am an ML instructor in Datalore Labs and a Data Scientist an The Inkers. I have been working on Machine Learning since 2013. I took Andrew Ngās second Machine Learning class. I am a big fan @rachel and @jeremyās way of teaching. Here to learn DL and the style of teaching.
Here is my LinkedIn profile: https://www.linkedin.com/in/vikram-kalabi-87504354/
~Vikram
Hello all!
I am from Ann Arbor, Michigan (originally from Delhi, India). I graduated with a degree in Mechanical Engineering, and have been working in the Automotive industry.
Machine Learning first caught my attention in early 2016 when I came across several articles on AlphaGo, and decided to take Andrew Ngās Coursera course out of the blue. My immediate reaction to neural networks was āWhoa! This is so cool!ā and I decided that day that I would want to dive deeper into this field and maybe pursue a career eventually. 
Over the past year or so, I have been learning through such online courses from Udacity, Coursera and the likes, trying to teach myself the basics of programming (albeit in a chaotic way). I recently finished the Udacityās Deep Learning Nanodegree. In my free time, I also participate in a paper reading group here at the University of Michigan, Ann Arbor where we discuss everything ML/DL.
I began Part 1 of this course early this year but got sidetracked by other things. Thanks to @jeremy and @rachel for this amazing opportunity with fast.ai. I am very excited to be starting the new version of this course. I look forward to interacting with you all and have fun while learning!
Cheers!
Hi, I am mani from Noida, India. I have been working as a software engineer for the last 3 years. My major expertise is iOS Development.
I was interested in machine learning and started on same early this year. I have taken a couple of courses and worked on a classification project using transfer learning.
With the recent software announcements in iOS, I want to work on interesting deep learning technologies and see how they fit in a mobile app.
I am very much excited and looking forward to a great course with you all. Connect with me here.
Hi, Iām Abdel and Iām based in Sydney, Australia. Iām in my final semester of my Bachelorās degree, and over the past year or so have been learning about machine learning.
I initially started with Andrew Ngās Machine Learning course on Coursera. I started reading and tweeting about machine learning, and shared things I found interesting. I then got offered a Research Assistant job with a small team researching artificial general intelligence.
I encountered fast.ai while looking for a better learning approach as it was a difficult for me to connect the concepts I learned from other MOOCs to practical projects, and I also wanted to explore different tools than MATLAB. I finished a couple of lessons in Part 1 and then applied to join the new Part 1! I am mainly interested in deep learning and reinforcement learning.
Hi all. Iām Arjun, and my journey into Deep Learning (or Machine Learning/AI) started 2 weeks back! Total beginner here.
My background is in web development, and I got into this when a client wanted to know if I could help him predict something about his customers. That was 2-3 weeks back, and I researched some literature on this, and came to the conclusion that a) the client does not have enough data because he is not tracking the right things at the moment b) the little data he has isnāt labeled.
Anyway, looking forward to learn as much as possible in the next 4 months from the 2 courses on fast.ai. Great to see this community of learners here. Think this community is really important as when the initial enthusiasm and passion for starting something new dies out in the first few weeks - and when things get really hard, itāll be good to have this community of fellow learners to remind us to keep doing. Came across this video on youtube on a kid learning to ride a bike for the first timeā¦ Hopefully all of us have the same enthusiasm as this kid at end of the course :). https://www.youtube.com/watch?v=9PzoxTgfRO0
Missed the session last Saturday - but going to start on this from today.
Thanks!
Arjun
Hello everyone
I am from Kolkata, India currently pursuing Bachelors in Information Technology (B.Tech in I.T. 3rd year). I first experimented with machine learning early last year. Since then I have done the CNN course by Stanford(CS-231N) , Machine Learning by Andrew NG(CS-229) and other online courses from Udacity, Udemy, Coursera, edX etc. Came to know about fast.ai few months back.
Looking forward to doing exciting projects in this course!
Special Thanks to @jeremy and @rachelā¦
I am very much excited and looking forward to have a great course with you all.
Would like to get connected on linkedinā¦
Connect with me here
My turn!
Iām Davide, from Italy. During my MSc in bioengineering I specialized in biomedical image analysis. Then I took a chance and did a PhD in Computer Science, focused on Image Analysis, where I started using machine learning to classify and segment biomedical images. Then, while working as a software developer, I started following Fast.ai and dealing with neural networks and deep learning in general. It really brought me back to loving computer vision tasks, and forced me to move back to that field. Luckily, I managed to find a position as Deep Learning Specialist now, mainly thanks to fast.ai!
The course made me start dealing with these problems that now became my daily job, so Iāll be grateful forever to Jeremy and Rachel!
Now, Iām officially in as International Fellow and really canāt wait to jump from Keras (which Iām using everyday at my daily job) to Pytorch!
By the way, it would be nice knowing if other Italians are on board for this part1v2. Let me know!
Hello all, Iām Robi from Italy and I am in for v2 of the course after having enjoyed v1 thanks to the fantastic online material.
My technical background is in electronics engineering, and Iāve always been fascinated by AI. I started practicing some ML coding only a few years ago after a career in consulting and sales. Last year I found a reference to Jeremyās DL course on a Kaggle forum and Iāve been following fast.ai since then.
For this v2 edition I am particularly curious about the potential of DL in NLP, which I would like to use for enhancing a search tool I am currently working on. I am also very interested in seeing hands-on the additional flexibility provided by PyTorch.
A big thank you to Jeremy and Rachel for creating fast.ai!
Hello @A_TF57 Ankit, I live in Toledo but I work in Ann Arbor (located at UM NCRC). Itās good to see another participant from around here
Iām interested in hearing more about the paper reading group you are participating in. My area is healthcare, so quite different from automotive! Iād like to connect with others in Ann Arbor working with deep learning.
Hope you enjoy the course!
Hello, everyone,
My name is Maureen and I am from Toledo, Ohio, USA (for those of you outside the US, thatās located at the tip of Lake Erie, one of the large Great Lakes on the northern border with Canada).
I have a background in health services research but I am a few years out from my PhD and am currently working in a managerial role for a large governmental health care organization. Last year I began learning Python, and after completing a series of programming courses on Coursera, I turned to machine learning courses and completed the UW series on Coursera (yes, I am a big fan of Coursera!).
I see now that the types of questions that interest me will likely necessitate a deep learning approach ā I work with tons of electronic health record data every day. Much of that is structured, but the truly interesting stuff is in the doctorās notes! Those are all text-based, and have yet to be fully mined for all their data goodness So I hope to do that!
Really looking forward to learning a lot along with all of you! I am so grateful for this opportunity, so many thanks to Jeremy and Rachel for creating this course and having a heart for students like us!
I am Hannan Ali from Lahore, Pakistan and I am very excited to join this course through an International Diversity Fellowship. Artificial Intelligence is a field thatās at best intriguing to me right now because the impact it can have in solving many of the worlds problem through automation. As a tech enthusiast, I want to be a part of building this future along with all of you. Currently, I have been working as a front end developer with a passion for open source and web standards development and truly believe in the democratizing power of the web and the opportunities it holds for all the people in the world. I would like to Thank Rachel and Jeremy for making this wonderful opportunity possible to learn about AI and appreciate their openness in making the field of AI more diverse by making it accessible top people all over the world. I donāt have much experience developing with Python but given that how beginner friendly and awesome this language is I hope to pick up the necessary constructs in the coming days through the great resources shared here.
Just yesterday, I was reading the book Hit Refresh by Satya Nadella and it increased my enthusiasm for this course even more as Satya talks at depth about the evolutionary changes our world will go through because of AI and how beneficial it can be for us, if we apply it to some of the biggest challenges we face in todayās world in Education and health and many other sectors.
Ending my intro with this inspirational quote by Alan Kay
The best way to predict the future is to invent it - Alan Kay
I look forward to learn in this course with you all, hope you all have a great day.
Regards,
Hannan
Hello,
I am from India. I started working with machine learning early last year. I have already done CS231N and CS224N (not completed) and read a few books. I am a little bit experienced with tensorflow and these days working with pytorch. Looking forward to learn new concepts and solve awesome problems.
I am super excited to use computer vision techniques to solve traffic problems (India specific, you know Indian traffic ) .You can contact me here
Hey hey Pranjal, Congratulations for ML at Amazon.
deeplearning.ai courses are really easy to follow, i doubt it will be a challenge for you after finishing fast.ai. However give it a try, if you are curious.
fast.ai part 1 and part 2 are way advanced compared to any Udactiy Nano degrees. You will learn nothing new about deeplearning or machine learning from self-driving car degree ;). If you are interested in RoboCars, other than DL focus on ROS, maybe checkout some online resources for learning ROS ), for e.g. http://wiki.ros.org/ROS/Tutorials
I didnāt realize that. What does Udacity cover? I havenāt tried it myself.
Udacity is offering a bunch of ānanodegreesā.
They cover CNN, RNN, GANs in the [Deep Learning Foundations Nanodegree-I] (https://in.udacity.com/course/deep-learning-nanodegree-foundation--nd101-infn1/) , [Deep Learning Foundations Nanodegree-II] (https://in.udacity.com/course/deep-learning-nanodegree-foundation--nd101-infn2/) courses.
Yeah I know the basic idea - Iām interested specifically in how fast.aiās courses are way deeper.
Hi @PranY , for reviews on Coursera deeplearning.ai and Udacity Deep learning nanodegree you can check out this blog by Vishnu Subramanian, a former international fellow of fast.ai. His blog got recently crossposted on kdnuggets as well. The blog won a silver badge for Oct 2017.
Hey everyone!
I am from Delhi, India.
I am pursuing B.Tech from Indraprastha University.
I started deep learning in middle of this year but my starting was not so smooth enough as I started with a neural networks course on NPTEL which had exhausting mathematics and was very boring. This made me fear the topic. Then I started watching sirajās ML videos which mainly focused on using different ML APIās. I was able to use different libraries like sklearn, keras etc. Which even landed me an internship and I grew overconfident. Not long after that i got to know how shallow my understanding was when i was asked to explain svm to a colleague of mine. I felt embarrassed at that time. I then determined to master this area and started reading blogs, taking courses (ngās ML, ngās deeplearning.ai, cs231n, cs224n, data science courses), reading books and started to understand the mathematics behind every topic. I started to implement those algorithms and developed a very good understanding.
Now I am looking for mates to start working on projects. I hope to form great bonds with great minds here.
I also want to start deep learning papers and write a few of mine too.
I am very enthusiastic towards this course as this covers the application part of the deep learning. I want to achieve great results with deep learning which will help remove the sufferings of the people.
Have a look at my work at
Though most of the things are under development.
I am a part of Udacityās self driving car Nanodegree and I have also done the Googleās deep learning offering on Udacity. I have completed term 1 which essentially is a introduction to machine learning and deep learning. I can talk about the self driving car Nanodegree. Most of deep learning content of the Nanodegree is from Googleās deep learning course. I think the course is a great introduction to deep learning and assignments they put up are decent. What sets the Nanodegree apart is the mentoring. The student is constantly monitored to know his progress and difficulties he is facing. But after all this I felt lost. The teaching I felt was very mechanical mainly because of lack off interaction with the instructor.
What sets fast.ai part are @jeremy and @rachelās top down approach teaching and interactive discussions. There is always new to learn here in fast.ai and instructors have always encouraged students to think in terms of application of the approaches and technologies learnt. Wiki pages are the best way of solidifying ones understanding. It feels like we are contributing to the course content.
Donāt get me wrong self driving car nanodegree is great but I never felt connected with the course. So I had to drop out after first term. This is just what I feel may be someone else can share there experience with some of the other deep learning nanodegree.