Greetings from Savannah, GA, USA. Reading through all of your posts is quite humbling. I certainly do not have the background that most of you do, but I’m trying to get there. I have a 20-year career as a teacher and assistant principal in public schools, currently at a middle school. I started taking Udacity courses in python programming a few years ago and completed the Data Analyst nanodegree last year. I learned about the Fast.ai program back in December and immediately began working through Part 1. I have completed part 1, v2 and have entered a few Kaggle competetitions to practice the skills.
I initially became interested in rigorous statistical reasoning after reading the works of Daniel Kahneman and Nassim Taleb. At that time, I began pursuing a more thorough understanding of data analytics, which has led me to deep learning and AI. I have become enamored with the field because I think it will be transformative across all sectors of our economy, and I want to be on the front side of that curve. I’m very much looking forward to continuing my study in Part 2.
Glad to be part of this exciting journey with you!
I have been developing software and helping others build software by developing APIs (most recently, the ArcGIS API for Python) and interoperability frameworks.
I’m excited by the potential of Geo-AI i.e. applying Deep Learning to solve GIS problems.
This includes processing satellite imagery for object detection and classification, semantic segmentation (such as land cover classification, identifying roads, etc). In addition to raster data/imagery, GIS involves analysis of large amounts of structured data, that can be enriched in multiple ways (eg, adding demographic and business data, geocoding, etc). I would like to identify patterns for applying deep learning to such vector data. I also see potential in applying deep learning for making maps - including detecting building footprints in maps, generating virtual cities using GANs, style transfer among the different types of vector and tiled maps, detecting roads in imagery for automatically creating vector maps for use with routing and so on.
There are applications of deep learning in routing and directions such as for predicting trip times. Deep learning can be applied for geocoding addresses in nonstandard formats in countries like India. I think the potential is immense and I am super excited about applying deep learning to GIS and taking it to the next level!
Here’s a video of me showing how Fast AI could be applied for analyzing satellite imagery.
In part 2, I’ll be applying object detection to datasets of cars instead of the Pascal VOC data. For the semantic segmentation problem (carvana), I’ll look into use it for detecting roads in satellite imagery.
I tweet on topics relevant to Geo-AI and am also looking for Interns in Delhi to work with me on Geo-AI - see my LinkedIn profile for details. Connect with me if you want to team up for some of these!
Hi, everybody. Glad to be part of the team that learn “deep”. My background is in Maths, Computer Science and Economics. In the last year, as a self-taught, I prepared myself in data science. Now I’m interested in some projects in applied maths and computer science in medical area and I want to launch some projects. After I will improve my knowledge in machine learning and deep learning I intend to cooperate with an university psychology clinic to apply all my experience to the blind, muti, deaf, autistic, people with Alzheimer’s disease. I’m able to cooperate with anybody that have some idea in this domain. GOOD LUCK !
I’m a freshman at the University of Pennsylvania on the East coast. I’m from the West coast, however, so I come back to San Francisco whenever I can. As a Bay Area native, I’ve been lucky enough to work at several well-established start ups around the area in High School.
I’ve just started my DeepLearning journey this year to supplement my University Education and am excited for the next 7 weeks.
Hi,
I’m so glad to be on this journey with you all! I’m Brian, a software engineer from Kenya. I’m taking this class through the International Fellowship program. I currently work for a health informatics company based here in Nairobi. I’ve added value to my current company in all sorts of ways, including writing low-level C code to make full-text search in PostgreSQL faster and more accurate – to maintain good UX when running our services in low-memory computers. I’m interested in building small, simple deep-learning-powered data products that add value to small businesses in a big way, and I have a few experiments running that I’m focused on selling soon. I like audiobooks, listen to podcasts while commuting, and love stories about people using science and rationality to find the best ways to improve the lives of the largest number of people.
In April I’ll be speaking at the first deep learning conference in Nairobi - thanks to fast.ai!
Hi there, Im a software developer from México with >10 years behind, I dont have a background on IA, but I think I could start making other things appart of my normal programming jobs.
I also started to use reference managers and things like that, I still have lots to study!!! I feel overwhelmed, but that sensation is nice.
Hello: I’m a data scientist with an extensive background in traditional statistics and I am looking to broaden my horizons into neural networks and deep learning. I’ve built a number of neural networks at work, and done 99.9% of the coursework from part I youtube lectures.
Thanks @init_27 for setting up this thread. Great to see so many people from different corners of the world with so many different backgrounds!
My name is David Mesterhazy. For me it’s the first time following one of the fast.ai courses. I’m joining as an international fellow and judging by all the positive comments that I’ve seen so far this was not a bad decision at all (even though for me this literally means sleepless nights). I’m really excited to be a part of this story.
A few words about myself: I’m a physics PhD with several years of postdoctoral experience. About half a year ago I transitioned from research to industry, driven by an interest in recent developments in deep learning and how they can serve to innovate industries and advance society. Most of what I know about ML I’ve taught myself between work and free time. Kaggle played its part too. Meanwhile, I work as a data scientist with IBM in Munich, Germany.
With this course I’m hoping to further my deep learning skills, to get more hands-on experience, and of course to have some fun along the way.
Hi everyone,
I am Saravanan Baskaran. I am a software dev with 10+ years of experience, I had worked on various parts of OSS from backend to frontend, from workflow systems to monitoring systems, from legacy sql trigger based eventing systems to the latest shiny things( angular and vuejs ). Have been playing with ML for a long time. I am doing the machine learning nano degree from udacity and currently at the final capstone project. I recently participated in the Narture.ai’s NIPS paper implementation contest and got selected to be featured in their site. The fast.ai courses are great spring boards for practical applications.
I write occasionally at my blog here, www.tutysara.net, recently I wrote about a fun activity I did, downloading machine learning flashcards from twitter . You can find me on twitter at tutysara
I am excited and grateful to be a part of it.
Many thanks to @jeremy, @rachel and everyone involved
I’m French but relocated to New York City three years ago. I’ve been a Math and Computer Science teacher for undergrads for seven years in Paris, and since I discovered deep learning (through the first part of this course) I’ve been really excited about Machine Learning. So far, I’ve mostly been teaching myself, between the part one (v1 and v2), blogs and online books. I’m thrilled to be part of the International Fellowship (thanks a lot Jeremy and Rachel to give us this opportunity) and looking forward to delving more into this subject.
My name is Alexandre. I’m from Quebec, Canada. I am a radiologist with a background in computer engineering. I started my learning in DL with the first fast.ai MOOC back in december 2016. Since then, I am totally captivated by this incredible field that is probably the seed for a computer science revolution, but also for a global scientific and social revolution. Of course, by default, I am interested in medical imaging and computer vision. I hope to connect with anybody interested in this vast application subfield as well.
Hi everyone,
My name is Guillaume, i’m from France. I am a backend software engineer with 7years experience, recently i’ve been focusing on backend performance analysis mainly on C++ backends.
I’ve enjoyed fast.ai part1 (v1 and v2 ) a lot via the MOOC and am quite passionate about AI now and its possibilities I i’m really thrilled to join this part 2 as an international fellow and thankful for the opportunity. hope to make the most of it
i have ideas at the moment about applying generative models to audio/music, and NLP for news reading, but basically interested in all that DL can offer. After part 2 i’ll likely spend some time studying reinforcement learning too.
Ok, time for lesson 8 soon, see you there
I live in Dallas, Texas and have spent most of my career with Data and Databases. Nowadays I spend time doing work in Spark, Hadoop, Cloud and so on. This is my second time with Fast AI and I am so glad to be here again as this is such a wonderful platform to keep me updated with the latest in Deep Learning. Also the chance to learn together with all these amazingly talented group is an awesome thing.
I’m from Los Angeles, but living in NY, NY. I’m an Inpatient Physician at Columbia University Medical Center. Before med school I co-founded a tech start up and learned a fair amount of coding during that time (all self-taught), but this is my first ‘formal’ CS experience. I’m excited to be here. I’m mostly interested using DL to improve patient experiences, health and safety and to improve hospital efficiency and quality. I’ve experimented with predicting hospital length of stay, for example, with some encouraging results.
My name is Adrien, I’m attending the in-person class. I was an ML engineer at Quora until January and I am now taking a few months off with the goal of learning as much as I can about research-grade deep learning so I can find a job in ML research engineering (fingers crossed! if you have opportunities let me know). Very excited about what I’ll learn in this class, I’ve always found that Jeremy had amazingly useful insights.
I am a Seattle AI enthusiast, and the Seattle ambassador for nurture.ai’s AI Saturdays.
I came to AI through my love of data. After playing with databases for a decade, I took a few years off from my technical career to experiment with coffee roasting in Kansas. It was a fun experiment, but not wildly successful, so I decided to brush up on my technical skills and go back to my technical career, and discovered that playing with data now means playing with AI. I started my learning journey with Coursera’s “Applied Data Science with Python Specialization” and I’ve just recently completed part 1 (v2) of the fastai series with the AI Saturdays group.
I’m particularly interested in language processing, both in nature and in technology.
Nice! Just connected my twitter to my medium account, hopefully this works. My twitter handle is the same as my Medium (and almost everywhere else), @AdrienLE.
Excited to check out that intro to matrix calculus: I always find that resources on deep learning math focus mostly on linear algebra and single variable calculus but don’t talk enough about calculus on matrices and vectors. Perhaps they feel like it’s a straightforward jump from single variable to multiple variables (“just do the same thing multiple times”), but it seems to me like there is a big psychological (and familiarity) barrier.
). Have been playing with ML for a long time. I am doing the machine learning nano degree from udacity and currently at the final capstone project. I recently participated in the Narture.ai’s NIPS paper implementation contest and got selected to be featured in their site. The fast.ai courses are great spring boards for practical applications.
. You can find me on twitter at