Hi Everyone!
I am an engineering student and deep learning practitioner. I got into deep learning a few years ago but am still very inexperienced when using a lower level neural network library. Last summer I did computer vision for the Monterrey Bay Aquarium Research Institute on deep sea creatures. It’s amazing how many fields stand to benefit from deep learning that haven’t received mainstream attention (such as field biologists!).
Looking forward to the first lecture tomorrow!
Nathan
Hello All this is Ankit from India,
Thanks Rachel and Jeremy for this beautiful platform and letting me into this international fellowship 
. I am very excited to start my journey with all of you.
I am currently working as AI Consultant for an Indian SI company and previously I used to working in DW-BI field.
I came to know about Data-Science/ML in late 2014 and completed few in-person courses to pursue my interest. After graduating in 2016 I have joined this field and since then solving ML Problems.
I came to know about FAST.ai in this January (I know pretty late 
), SInce then I covered Part1V2 videos through self-paced learning and as I come from R background so shifting my gears towards python.
I have joined this course as in day to day life I don’t get much time to implement ML solutions from scratch and with the help of fast.ai I want to build Deep learning solution which helps to eliminate some real issues from society.
Also, After reading Rachel blogs and students like @init_27 (Great fan of your work) blogs on Medium trying my way to write first blog to contribute the same.
Hi everyone,
I’m Armineh, a research scientist with a background in NLP and traditional ML. My main experience with DL comes from auditing Part1 (v1 and v2), so I’m really looking forward to attending the live sessions for Part2.
I’ve worked with social data, news content and legal documents for most of my research career. I see a lot of opportunity for applying DL models to augment unstructured content using semi-structured data. Currently I’m working on a personal project that involves using metadata from GDELT to predict the geolocation of tweets.
I’m really excited about this opportunity to work through this course with all of you.
Best,
Armineh
Hi Everyone,
I am an analytics practitioner fascinated by AI quite possibly because I grew up on sci-fi movies like Terminator :-). Have consulted senior executives in large enterprises on application of ‘traditional’ analytics mostly in marketing. Almost always disappointed by the need for ‘simple’ solutions that management can understand as opposed to the right solution for the job…
For quite some time in my career, had relegated myself to a fate that the ‘cool stuff’ is only for PhDs or a select elite…
Over the last few years, have discovered open platforms like kaggle, fast.ai that make the state of the art accessible to almost anyone who is curious enough. Forever in debt to people like Jeremy/Rachael for making the effort to share, not hoard this knowledge…
Convinced that anyone can become the worlds best if they follow this recipe…
Looking forward to new adventures in neural networks in this course
regards,
Chandan
Hi everyone,
I am a PhD student in atmospheric science at LMU Munich. Normally, my research is on stochastically representing physical processes such as turbulence and convection in weather and climate models. Currently however I am at UC Irvine building the first deep learning climate model.
The fact that I even got to this stage is largely due to fast.ai. I feel like I learned more in the year than in all my university courses together. So now I am super-excited about being part of the latest installment.
See you all tomorrow!
Stephan
Hi,
I’m Kanishk, working in the analytics team of a bank in India. I have a masters in business and started teaching myself to code after I started my current job. I started with R and slowly moved to python and have been completely hooked on to data science using open source in the last 3 years.
Fast.ai has been a huge help in helping me transition from a non tech background to doing the work I am currently doing. All from automation of pipelines to snippets of computer science that I won’t have found otherwise - I’m taking the course live for the first time and I’m sure it’s going to be an amazing 7 weeks!
cheers!
Hello @raspstephan - I just read your blog on Deep Learning resources where you recommend your peer weather scientists to follow the Keras based version of the fast.ai version1 part 1.
Did going through Part 1 V2 change your opinion?
Hello,
It’s great reading about all if the awesome stuff everybody is working on. I am in Omaha, NE and I am focusing on how to apply the knowledge I learn from fast.ai and actually provide value to companies. I have been teaching myself how to build a website from the ground up using Django which is a python framework. Since everything we do is in Python I think I will be able to pair this with interesting models to provide people with a simple way to interact with some of the more interesting models. Very excited to start p2v2 tomorrow and can’t wait to put everything together!
Thanks,
Kevin
Hey there.
Everyone here seem to be way more qualified to be taking this course than I am, but I’ve become incredibly interested in what deep learning can offer. I learned a whole lot going through Part 1 and Andrew Ng’s deeplearning.ai videos on youtube (did not take his MOOC).
I graduated from Berkeley with a degree in computer science (my first exposure to ML / Neural Networks), then started working at Pandora as a software engineer on the consumer electronics team (lots of languages and platforms, some interesting problems).
Seven months ago I co-founded a startup focused on providing a better experience for teams working with various dialects of SQL. I joined the cause to see how a company is built, learn more about the business side of starting a company, and continue to learn everything I can about good architecture and software engineering (and I believe in the product).
There are so many problems to which I’d like to apply deep learning, and I’d like to make sure I do it well. I can’t wait to get started!
Jason
Hi everyone,
Late to the party here, but hopefully not too late.
I am by no means an expert in ML/DL, but has been actively learning it since end of 2016. Initially by taking cs231n online, and then enrolling in Udacity Self-Driving-Car nanodegree (completed Oct 2017).
Went through fastai part1v2 as MOOC in Jan-Feb 2018, and super thrilled to join the upcoming part2v2 remotely as int’l fellow.
Background was (ages ago) in image processing, signal processing, so quite comfortable on the math/linear algebra/probability part of ML/DL.
I am based in Jakarta, Indonesia – so this is like my technology therapy/sanctuary in a hectic and super bustling city. Highly interested in developing ML/DL applications either in radiology assistance, agriculture or traffic management related. Seriously looking for angles to apply ML/DL in practical application.
Looking forward to interacting with everyone in this course.
Cheers,
Otto G
Yeah that was me - I figured that telling someone they can’t join because they’re helping a refugee family wouldn’t quite be in line with our values
@jeremy, you rock.
I didn’t get any machine problems done today either, but we delivered another small load of donated furnishings, talked about budgeting, helped them find the local ATM, do a balance inquiry, deposit some cash, refill their transit cards, take the local rapid transit three stops, and take a bus home. 
Here’s a writeup on the first nine days of our sponsorship experience.
@vikasbahirwani Thanks for browsing through my website.
This is a tough question. The main reason why I suggested doing version 1 is that, at least from my personal experience, most of the applications of deep learning in atmospheric science are not traditional image classification or NLP tasks. The fastai library on the other hand seems to be geared towards best practices in these most common areas.
That’s why I found the Keras approach a little more useful. I think I still stick with that, but of course doing both versions would be the best 
. So much information in each lesson!
Many thanks for sharing - that’s really interesting. I remember reading an article last year about Canadian families that were finishing the first year of refugee sponsorship; it sounds like “letting go” at the end of the experience can be hard too!
Hi everyone!!
I am Priyadarshini Panda (most people call me Priya). I ll be a part of the Part2v2 course through the International fellowship program. I am a PhD student at Purdue University, West Lafayette, USA in ECE. My research focus has mostly been on spiking neural networks (which are the biologically plausible counterparts of traditional ANNs with bio-inspired models for neurons/synapses/spike-based learning). I am currently in my 4th year and wanted to do more practical aspects of Machine Learning. In the past few months I have looked at some of the courses/MOOCs on Deep learning mentioned by most of you.
I am really excited to be a part of this program. I recently entered Kaggle competitions (Thanks to Part1v2) and have gained so much of knowledge and experience just fiddling through different setups. I want to get some real hands-on experience and to be honest get over my ‘so-called fear of coding’. After taking Part1, I could read and understand traditional ML papers with lot more ease and went on to implement a few ideas like Grad-CAMs and Adversarial attack effects from scratch. Big thank you to @jeremy!!
Looking forward to learning more with this course!! And hopefully I can utilize my knowledge to steer my last few semesters of PhD in a more practical direction.
Greetings from Ottawa, Canada. Very cold right now, -20 Celsius (-4 Fahrenheit), so looking forward to cozying up to deep learning.
I am a newbie to coding, but I will do my best to contribute to the forum. Most of my career has been in statistical analysis in either SPSS or Excel. I dig data, especially on the crime fighting side. You can see my whole CV on LinkedIn if interested: linkedin.com/in/stephenrimac/
Cheers!
Stephen
Hello everyone,
Having spent most of my career in IT application development, I have really enjoyed exploring the Fast AI approach to DL and ML and considering ways in which this technology can be used to solve everyday problems. Thanks to Jeremy and Rachel for helping to make this mystical domain accessible to us mere mortals 
. I would also like to thanks Perth Machine Learning Group for the local support here in Perth, Western Australia.
I really enjoyed the PyTorch version of Part 1, and how this helped with the identification of rooftop solar using aerial photos. I am looking forward to the next 7 weeks of Part 2.
Thanks,
Bevan
Hi Everyone,
I’m really excited to be a part of this course with all of you!
After graduating college, I spent six years working as a software engineer for various startups in Silicon Valley, before becoming a remote software contractor. A little over a year ago, I settled down in Hong Kong to be with family and have grown in love with the city.
I split my time between work (mostly full stack web and mobile), machine learning, and family. I took Andrew Ng’s MOOC, the Fast.ai ML v2 course, and Fast.ai DL p1v2 course. I’ve also watched the p2v1 videos, and am excited to learn about all the advances in AI since then!
Can’t wait for the first class tomorrow (or tonight, if you’re in America)!
Tom
Hi all,
My name is Roye and I’m from Israel too. I’m currently working in computational genomics, having submitted my PhD thesis a few months back. I am late to the ML/DL game, having focused much more on data structures (i.e., big data) and genomics during my PhD. I’m very eager to make the transition and add this stuff to my toolbox, and I think part 1 was a great start.
I think there are ton of areas where DL can be applied and I am really looking forward to this experience.
Many thanks to Jeremy and Rachel and good luck to all!
Cheers, R
