Hello,
I’m Giacomo Vianello, Senior Data Scientist at Cape Analytics, a deep-learning centered company that extracts intelligence from aerial images for the insurance and real estate world.
Before that I spent 10 years doing gamma-ray Astrophysics at Stanford University as Research Scientist.
I love coding, solving problems and building things. Beyond learning as much as I can, I want to take this chance and find people to explore a crazy idea of mine centered on optimizing training using the uncertainty information.
Hey Everyone!
This is Jayesh Vasudeva, currently pursuing my Bachelor’s in Instrumentation and Control engineering from Netaji Subhash University of technology, New Delhi, India.
I am glad to be a part of the course and participate in the awesome community, I have done a couple of projects in the field and im looking forward to build some more with the fellow course members and guides.
My aim is to come up with amazing projects and publish papers on them, besides that I also aim to host a personal blog series.
Hoping to learn a lot !
Hi,
I am Ganesh Bhat, based out of Mumbai and currently working on Analytics with an Automotive spare parts manufacturer. I have 13+ years of work experience and have been working primarily on Machine Learning using python for the past 2-3 years.
I have primarily learned about Deep Learning using CNN in Andrew Ng course on Machine Learning but beyond that, I haven’t actually used it much yet.
My expectation from the course is to learn about deep learning algorithms and how they work for textual, numeric, image and video data types. Also, how to deploy these from end to end perspective is a crucial learning that I hope I will get out of the course.
Hey guys, I’m Nate (@phi_nate) I’m a physicist at CERN / Lund University in Sweden, and love all things statistics + deep learning!
My recent foray into machine learning (or rather its superset, differentiable programming) led to this library, which uses the jax library to create a fully differentiable pipeline that lets a neural network (or any parametric model!) get trained with respect to a downstream goal.
I’m actually a massive fan of this functional nn approach – not sure if @jeremy or others have thoughts about ways jax could fit into the fastai universe, but I’m certainly here to help facilitate that
Because we’re in trying times of isolation, I’m likely going to be livestreaming on twitch around EU timing while I attempt to build projects based off of the principles of each lesson, so if you want to come and hang out there, I would love to tele-meet you!!
Looking forward to continuing my ML journey with you all! Thanks to the whole fastai team for all that you do
I am Dhwani (@Dhwanib) I am a Data Scientist at a FinTech company. I am an Economics graduate and dabbling in Deep Learning.
I am very excited about application of Deep Learning in areas of Economics such as Auctions. I am also trying to solve the puzzle of Causal Inference using probabilistic graphical models and econometrics theory.
Looking forward to meet you all and learn together!
I’m Niyas Mohammed- a research engineer at a tiny startup in India that works on natural language processing. I’ve been with fast.ai throughout the years and every edition kept bringing new challenges and undoubtedly when I finish each lesson, I feel a little wiser.
This year my goal is to keep pace with the course as it runs (I usually spend a lot of time playing with the code and lag behind by around lesson 3 - and yes, I know Jeremy says I don’t have to understand everything in one go).
I also want to make myself useful to the community this time- so I want to share my findings here in the forums and offer help whenever possible.
I know we’ll learn a ton this season!
Happy learning
I’m a back-end engineer in the SF Bay Area and am interested in moving towards ML and statistics. I’m also a chronic bottoms-up learner and want to break the habit by learning something top-down . I became an engineer about 2.5 years ago thanks to MOOCs and books, so I have high hopes that this online version of the in-person class will still be a great experience!
My other passion is music, so I may look to incorporate that into the projects.
Heyyo! I’m a biomedical engineer playing the role of a software/data engineer leaning into data science at the University of California, San Francisco’s Center for Digital Health Innovation. My team at UCSF is focused on applying multi-modal data to health problems right at the intersection of a slew of very interesting digital health problems. Here’s joining a crowd of interesting folks solving the world’s most interesting problems!
Hi! My name is Jaime. I am originally from Ecuador . I am currently writing from San Francisco’s Embarcadero. I am a Data Scientist at a startup here in the city that specializes in gamification of marketing offers. My main area of interest is Personalization and Optimization in environments with partial information.
This is my first step into Deep Learning and I am looking forward to using the fastai library to learn about better ways to represent behavior (recency, frequency, monetary) through embeddings to increase the impact of personalization and ultimately in customer’s lifetime value.
Im also looking forward to collaborate with the fantastic group of people we have in the cohort. Cheers!
Hi there,
I’m Gautam from Stuttgart, Germany. I work for an automotive supplier as a project leader. My regular job is entirely “traditional” (electro-mechanical) engineering-related but I have some a background in numerical programming from my PhD work in finite element modelling (for magnetic materials). I’ve been following fastai for a couple of years now and mostly worked on deep learning as a hobby (online competitions, automating stuff at work).
I am a big fan of the fastai approach to teaching and spreading knowledge and I hope to help in some way by taking part in this course.
While I look for opportunities to apply what I’ve learnt in the course in my work (automating engineering tasks and processes), I also want to work on applying deep learning to diagnosing Progressive Supranuclear Palsy (PSP). PSP is a rare neuro-degenerative disorder that is difficult to diagnose (typically they use brain scans and other markers, my father suffers from it so I have been reading up quite a bit on the topic and I think there is something here that AI can help with).
Lets see how things develop. Perhaps the course inspires a couple of ideas
within me .
I’m really looking forward to working through the course and getting to know everyone
I’m currently the lead data scientist at a startup working in the talent acquisition / recruiting space. I spend WAY too much time working on stuff like data modeling, CI/CD pipelines, API code, and all the “not-so-sexy” parts involved in getting machine learning models into production. I’m really excited to take this course and get back into the fun stuff!
I started my career with a bachelors in Finance, and thought that I was going to use a linear regression from my econometrics class to make millions of dollars in the Stock Market (spoiler alert, that didn’t happen). I went on to work as a business analyst on a data warehouse team at AvidXchange (a FinTech company), where I worked for a few years. I moved into a data scientist role, and then eventually jumped ship to another company and changed industries. I recently completed a masters program in data science that had a strong focus on optimization algorithms and evolutionary computing (genetic programming, simulated annealing, etc.) and this is one of my strong passions.
I found FastAI through the TWIMLAI (This Week in Machine Learning and AI) Slack group, and recognize a few friendly faces on the forums! I’ve followed along with most of the previous courses (ML for coders, DL part 1 & 2 v. 3), although I haven’t done enough practical coding. I’ve got a couple side projects I’m hoping to work on as we go through the course, and can’t wait to get started! I’m hoping that I’ll be able to contribute a couple interesting datasets and hopefully help out with some deployment patterns / code we’re using to ship stuff into production.
I’m Phillip Chu. I first heard about fastai in late 2019, and was deeply impressed by the techniques, approaches, and the generous service to the community.
The new fastai v2 is even more exciting, as it condenses decades of best practice/wisdom in software design. It is truly a labor of love. The functional core seems so powerful, and yet is general enough for applications other than AI model training. The integration with rapids.ai also spells great potential. I very much am looking forward to learn from/work with the many experts from different domains here.
My background is in system/distributed/network programming, with a slight affinity to performance tuning/investigation.
As I waded through the previous version, the fastai v1, I built a Jupyter notebook extension called Ddip — for “Distributed Data with IPyparallel” — an exercise to bring distributed-data-parallel (DDP)/multiGPU training mode to fastai’s course v3 Jupyter notebooks, with minimal changes (it wasn’t supported before, as mentioned here.) Not very polished, and I plan to port it to fastai v2 library soon.
I’m Victor Vargas, I’m originally from Mexico City, currently living in San Francisco as a Software/Infrastructure Engineer and Manager been coding since 2009. I work for UCSF School Of Medicine Technology Services.
I started with Fast.AI in 2019 thanks to working with @pavgup here with me in the same course I followed all the course for DeepLearning for coders and also the computational lineal algebra but the later was pretty tough I don’t have Machine Learning experience but I’m willing to learn a lot and try things out I’m really impressed and fascinated with how it works and I love to learn new things. I know some of python so feel free to reach out if I can help or know the answer I’ll try to help as much as I can.
I’m interested in learning Vision and also NLP. I think both of those are the most impressive pieces of development and also that could definitely help in resolving some of the world most complex problems.
Hi everyone! I’m José and I’m a Lead Data Scientist at everis in Lisbon, Portugal.
I have an engineering background, having spent years programming (which I still love), database design and tuning, and software architecture. Solving problems has been a passion for as long as I remember, and am passionate about machine learning since way back in 1996 when I created a speech recognition system using HMMs (hidden markov models).
I wholeheartedly jumped at the chance to participate in the 2020 class because Jeremy has such a brilliant way of teaching, and I profoundly admire the work his team has done with the software and community. I 'm looking forward to lending a helpful hand anyway I can.
I’m very interested in training / getting better results in image detection, neural network visualization and nlp in Portuguese.
Hi everyone, I am a software engineer from Berkeley. I work on machine learning projects at scale Couple of fun facts about me: I trade more than 10% of the daily volume at a mid-size cryptocurrency exchange doing market marking. I love to hack and build prototypes. I was disappointed with the lack of information online and hacked a web site / backend last weekend to track the covid-19 situation in the US. I’ll add growth stats in the coming weekends as I collect more data.
Hi Everyone, I’m Vamsi
I work for the Analytics group at a DTC company, Allbirds, out of San Francisco.
Looking forward to learning applications of Deep Learning and applying them to problems with limited datasets in a smallish company.
I’m also at @vamsified
I’m Paul, based between London, UK and Osaka, Japan. I’m really excited to have the opportunity to participate in this course. I’ve been a keen, albeit somewhat tentative follower of the previous fast.ai course content.
My background has primarily revolved around trading financial instruments, but I switched to the FinTech sector in 2018 with more hands on coding experience. I also developed an iOS game (as a hobbyist) during the ‘app goldrush’ days of 2011/12 that spent a few weeks at the top of downloads chart.
I have wide ranging interest when it comes to deep learning, but I am particularly keen to consider ways it can be used to help those with autism (including my son, 3) achieve their full potential in life.
Thanks,
Paul @PDiTO on Twitter - never post, only read, but hoping that will change.
I’m a physicist at CERN / Lund University in Sweden, and love all things statistics + deep learning!
- and yes, I know Jeremy says I don’t have to understand everything in one go).
. I am currently writing from San Francisco’s Embarcadero. I am a Data Scientist at a startup here in the city that specializes in gamification of marketing offers. My main area of interest is Personalization and Optimization in environments with partial information.
I don’t have Machine Learning experience but I’m willing to learn a lot and try things out I’m really impressed and fascinated with how it works and I love to learn new things. I know some of python so feel free to reach out if I can help or know the answer I’ll try to help as much as I can.