Introduce yourself here

Please tell us a bit about yourself, where you are in your deep learning journey, and what you’d like to get out of this course! :slight_smile:

Edit: including your twitter account would be great so folks can follow your work! You can edit your post if you’ve already posted. My twitter is @jeremyphoward. Twitter is the main way I keep up with what’s happening in the DL world.

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Hi everyone, I’m Pierre!

I’m a French engineering student, starting a DL internship mid-February. I started my DL journey with fastai v3 and absolutely loved it. Although I didn’t do much DL in the last 5 months (except recently for a week long hackathon), I look forward to getting back on it, especially with my internship starting soon! I owe this internship to fastai so I look forward to giving back to the community.

During fastai v3 I made a few blog post on my personal blog, the most popular of which was on understanding the math behind Xavier and Kaiming initialization. I’m not sure yet but I think I’ll try to do 1 blog post per week during fastai v4, mainly for complete beginners (but maybe a few on more advanced topics as well?).

I really look forward to discovering fastai v2 during the weeks leading to fastai v4, as well as the course itself!
I’ll also have to take a closer look to that wonderful nbdev panacea I keep hearing about on Twitter.

Overall, couldn’t be happier to be here! Thanks so much for the opportunity and the hard work behind those courses Jeremy, Sylvain and Rachel!

Edit: as requested, my Twitter @PierreOuannes

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Hey all, my name is Zach :slight_smile: I am a Software Design and Development major with a minor in environmental science minor Undergraduate student at the University of West Florida! This year I’ve become very familiar with the fastai code, fastaiv1 and v2, and started a v2 study group! Along with this I’ve done a number of projects (and a paper soon!) utilizing the fastai library! For those wanting to jump a bit ahead you are welcome to come join, we’ll wind up finishing Vision when the course begins :slight_smile: (here is the link: A walk with fastai2 - Study Group and Online Lectures Megathread) Thanks to fastai this summer I will also be interning at Novetta working on machine learning applications :slight_smile: Can’t wait to learn with you all!

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Hello! My name is Fabio. I am a brazillian independent business consultant that relies heavily in Data Science.

I have a graduate degree in CS and a full time MBA, both at the Federal University of Rio de Janeiro. I worked for PWC and IBM BCS in the beggining of the 2000s in Brazil implementing BI solutions for big National Telecom Companies.

Can you believe my first Neural Net experiments date back to 1997? Back then, I built a simple one hidden layer ANN using a brazillian Neural Net implementation called SIRENE for predicting car engine grinding based on the concentration of chemical elements in the oil of 5.000 cars. It took a few days to run a model and we kept watching the error dynamic charts slowly decreasing all night. I remember using momentum parameters to try to avoid local minima. Fun but tiresome times. Since then I fell in love with Data Science and have been trying to apply it whenever possible.

I watched almost all Fastai MOOCs since the first one (now starting 2019 part 2) and I am very grateful to Jeremy, Rachel, Sylvain and all the fastai contributors that allowed me to step up my Data Science skills! Now I want to make a deep dive and be able to develop customized solutions to some problems I face, like trying to apply UDA to ULMFIT and to speech recognition (portuguese is scarce in speech data).

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Hi everyone! I’m John, and I am an astronomer working at the intersection of galaxy evolution and computer vision :slight_smile:

I started with the Fastai 2018 course, which got me very excited to embark on the deep learning journey. Within months, I was using the fastai v0.7 code to achieve spectacular results on modelling the products of chemical enrichment in galaxies (if you’d like, feel free to check out the paper). Some of this work ended up becoming part of my PhD thesis, and it also helped me find an amazing postdoctoral research position! Recently I used the fastai v1 code to investigate some of the ingredients for star formation and galaxy growth (see paper here). It’s been such a fun journey, and the two most recent versions of this course have been wonderful.

I’m hoping to get that kind of familiarity with the newest codebase: fastai v2. In particular, I want to be able to work flexibly with the pipeline so that I can directly use astronomical data products without loads of pre-processing steps. Based on the awesome work that the team has been doing, and the early study group by @muellerzr, it looks like the updated code will be better than ever. Thank you Fastai!

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Hi, I’m Paul. I do weird stuff for people that mostly involves computers.

Most recently I’ve worked a lot on politics and I wrote VoteFlipper using parts of fast ai and parts of some other stuffs.

Ethics aside, it’s the only one in the world like it and I built it for two reasons: to help stop disinformation and to help smaller candidates run races without the help of expensive consultants.

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So cool! Congrats!

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Hi all, I am Francesco. Nice to e-meet all of you :smiley:!
I am a currently a Data Scientist (DS) in a Finnish fintech company based out of Luxembourg, taking care of application credit scoring models. Before that, I worked in a ride-hailing startup in Estonia, in Amazon Kindle, and in the aerospace and military industry for a while too.
I am originally from Italy where I studied Chemistry in college, only to realize, less than one year after finishing my studies, that tech was my actual passion! This is when I got in touch with ML first, with the awesome course by Andrew Ng on Coursera, to eventually end up with fast.ai MOOCs.
In my spare time, I try to experiment as much as I can with anything related to DL and ML, avidly writing everything up on my blog and productionizing (hopefully) cool stuff on VisualNeurons.com.

It is so great to be here! Thanks again to all of you!

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Hello All,

This is Sandeep Singh here in Palo Alto. I am a software developer turned Deep Learning Practitioner. Also, I am doing Fast.AI Part1 3rd time. Thanks to Fast.AI, I am running AI at startup named beans.ai here in Palo Alto.
In my current role, I am mainly doing Computer Vision tasks at low-resolution satellite images. We are building database of things seen in maps, which only could have been imagined few years ago.

I am so excited to attend this course again and get engaged with awesome community of fast learners here!

Cheers!

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That sounds really neat. Looking forward to hear more of your success stories. :beers:

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Hi everyone,
My name is Kieran I originally studied Physiotherapy and worked in this field for 8 or 9 years. In around 2017 I started to teach myself to code out of curiosity and developed a great passion for it. I took these skills and was able secure a full stack developer position in 2018 and since then my interest has been focussed mainly towards machine learning in particular using fastai.

I currently work at a travel startup in Canada and split my time between full stack development and writing machine learning solutions to travel problems.

I am really looking forward to helping new learners get to grips with these concepts the same way others helped me when I first started.

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Hi everybody,

I am Suvash, currently working as a software engineer at a bioinformatics software company, where we build software that runs analysis on bacterial genome (sequence files) at scale to tackle public health problems involving antibiotic resistance. I’ve previously built software systems at various other companies in Fintech, Adtech and Classifieds industry.

I’m excited to refresh a lot of my stale DL knowledge, hopefully get over the hump where I use these techniques on a frequent basis, use DL to solve some problems in the genomics/bioinformatics space, and overall just have fun while learning it.

Also, looking forward to run into old familiar faces as well as meet a lot of new ones here in the forum. :beers:

@suvash (on twitter)

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I everyone!

I’m steeve and I’m a strange mix of machine learning architect/engineer. I hold a master degree in computational physics, a master degree in financial engineering and because I had nothing better to do in a summer, I decided to pass the first 3 actuarial exams just for fun.

My quote:
If you stop learning, you start dying!

See you soon!

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Hi all, I’m Dave!

Very happy to be joining everyone for this next version of the course. I’m currently consulting for the World Bank / Global Fund for Disaster Reduction and Recovery on geospatial ML for disaster risk management. Small plug: I’m an organizer of the Open Cities AI Challenge, an active competition (until 3/16!) with a novel, accessible dataset of building footprints and drone imagery spanning 10 African cities and $15K in total prizes for semantic segmentation and responsible AI ideas for improving disaster resilience. I also work independently on AI for climate change projects and plan to put more emphasis there going forward.

My deep learning journey is essentially a fastai journey, having taken almost every remote version of the course with a happy exception of doing part 2 (2018 version) in-person. I don’t have a coding background (formal education is in bio/medicine/business) so these courses and community have doubled as excellent resources on python programming and software dev. This is one of the most useful educational experiences I’ve engaged in (with more than enough formal schooling to compare against!). And it’s through my learning projects done during the courses (i.e. coconut tree detector, Zanzibar building segmentation with drone imagery tutorial) that helped me get to my current work. It’s a testament to the quality and depth of the teaching that I learn so much and still feel like I barely scratch the surface in every iteration.

As a stretch learning goal for this course, I would love to work with anyone interested on creating a geospatial submodule for fastai2 in the style of fastai2.medical.imaging. This may be too time-ambitious (at least for me as a recently new parent) but as a minimum, I’d like to put together some demo notebooks showing how to take in some useful open geospatial datasets and achieve good-to-great results on common geoML applications like land cover mapping with the power of fastai2.

Looking forward!
Dave

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Wow, your work is amazing and it’s crazy to imagine that you don’t have any formal coding experience. I’ve learned a lot from your Zanzibar geo segmentation (+ interpretable heatmaps) write up, and I’d love to try something similar.

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Hi everybody, I’m Vijayabhaskar (you may call me Vijay). I’m currently pursuing my Master degree in Computer science and engineering here at Chennai, India. I’ve been following fastai courses since v1 of the courses made available. Been a Keras user for a long time, looking forward to dedicate my time learning fastai2 this year. Even though a member of fastai forums for 2 years, I’ve just started being active here, So I’m surprised that I have been invited to join for fast.ai Live: Practical Deep Learning for Coders 2020. Thank you for inviting me.

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Hello Everyone,

I am a Digital Analytics developer at a software company. I attended the practical deep learning course in 2018 when fast.ai v1 version was released. I must say, it was an amazing experience. I had zero knowledge of deep learning before 2018. The course was well constructed.

I am currently taking machine learning course from fastai 2018. The course is awesome as expected. Currently I am understanding how feature importance and Random forest works to help solve major problems in industry.

Hoping to take most out of this course.

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Hi all, I’m Hari,

Really happy having got this invite for part 1 2020. I have started the Fastai journey in 2018 and since then have been associated with this group every single day.

There are a lot of experts in this group , Dave for geospacial , oguiza for time series and many many more. I have learnt a lot from all these people.

I work as a AI arch for a financial services company.

Thankful to Jeremy , Rachel , Sylvain and all other contributors to fastai library.

@jeremy , would be great if you could include graph networks as a topic. thanks.

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Hi everybody,

I’m Kevin Bird. I’m currently working for a trucking company splitting my time between data science and robotic process automation. I also work on a lot of Kaggle competitions with Hiromi. I have taken all of the fastai deep learning courses and have been paying pretty close attention to v2 of the fastai library. I’m hoping to get a better understanding of the new library through version 4 of the class. I am really excited to extend the lessons this course provides and explore new ways to contribute. Every time I take this course, I get a better intuition and also get more confident in trying blah.

I’m looking forward to collaborating with everybody on here and am really excited to see what kinds of projects are generated from this years cohort! Please feel free to reach out with any roadblocks, questions, or ideas! I really enjoy talking through ideas with people and discussing possible collaboration opportunities.

Thanks to the whole fastai team for everything you’ve built. I wouldn’t be where I am today without the great community and lessons that you’ve put together. I recommend fastai to everybody that asks me for how to improve their data science skills. It really is a great resource no matter where you are in the learning journey. Really excited to see what we cover in class this semester!

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Hello everyone! I am Sayak, a Deep Learning Associate at PyImageSearch. I have been getting benefited from fastai (in all its entirety - lectures, walkthroughs, community everything) for some time now (I seriously got introduced to it in late 2018 and I have got hooked). Although my professional work includes TensorFlow and Keras but the concepts I have learned from the lectures and the community have been tremedously beneficial for me.

Off the work, I give talks, write blogs and interview my machine learning heroes. I would love to get connected - sayak.dev.

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