Introductions thread - please say hi!

OK Jeremy,
Thank you for your reply. I will read this new paper and also other pre-reading we received by email to have a general understanding and be quite ready for this Monday.

Hello everyone,
I am Sai Kiran. I’m a data scientist from Hyderabad, India who is deeply passionate about machine learning and AI in general. I have worked on some interesting problems on text, emotion detection via speech etc.

  • I have missed the part-1 but have taken it offline.
  • I would be pursuing masters in computer science with emphasis on machine learning and AI this fall. I felt like taking a short break before embarking my masters. So currently I’m focused on learning and mastering deep neural networks. I am also enrolled in Udacity’s self driving car nano degree.

I am thrilled to be a part of the second part! Looking forward to learn some cool stuff!!

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Hi all!

My name is Sourav Dey. I’m a machine learning engineer here in San Francisco. I’ll be joining in person for this DL pt 2. Really looking forward to it.

  • I was formerly at Nest/Google, but left to start my own “AI product development studio” called DataHoliks last year. In the last half year, we’ve worked with our clients to develop the machine learning products that are already on their roadmap. It’s been a crazy journey, but I’m really loving it.

  • I’m originally from the Midwest and went to school on the East Coast at MIT for both my undergrad and PhD and have been living here in California for the past 8 years working for various startups doing data science, SW engineering, and beyond.

  • I found out about this course from HackerNews. I was looking to level up in all things deep learning and was going through the cs231N course from Stanford, but when I saw the lectures for this course – I knew it was exactly what I needed. I wanted PRACTICAL learnings of how to build modern deep nets.

  • I am hoping to do MANY fun things with deep learning. Very specifically, I am leading a team (for a client) to participate in the DARPA Spectrum Collaboration Challenge to develop “cognitive radio” applications. The goal of this DARPA grand challenge is to use modern machine learning techniques to understand the radio spectrum environment and “play nice” with other radios in the band. My plan is to use CNNs for doing the spectrum understanding and DQN based reinforcement learning to do the “playing nice”.

  • Also, another application I’m working on with a startup I’m advising is to use deep learning for gift-giving called Glifft. Specifically, I’m looking to build a “taste classifier” to automatically pare a product feed of millions of items down to the ones appropriate for the catalog. Also, an even bigger project is to help their team build a huge hybrid (content-based and collaborative-based) recommender system that personalizes gift recommendations based on what customers are swiping left and right on.

  • I am passionate about many things. I love math and software and all the amazing applications of it. I love learning about this stuff from deep learning, to optimization theory, to information theory. I’ve been doing this for years and can’t imagine doing anything else. At the same time I am really passionate about the the outdoors and nature. I love hiking, backpacking, climbing and really any activity that gets me outdoors into the wilderness. My dream is to one day combine these two loves into a project – but I haven’t found that yet.

  • Though I’m of Indian descent and grew up in the Midwest for the most part – when I was a teenager my family moved to Asia. So I lived (and went to international school) in South Korea, Malaysia, and China. It was definitely an adventurous childhood – but awesome.

Really looking forward to working together with the rest of you all on bleeding edge deep learning!


Hi - I’m Otto. I’m joining in person in SF. I currently work as a data scientist at Fitbit doing a mix of ML and experimentation. I got my undergrad degree in economics from Harvard and spent several years in the Marine Corps prior to getting into my current line of work. I’ve been using the python ML stack for a few years and before finding this course, I had been frustrated by the learning curve to get into deep learning. I’m thrilled by how much I’ve learned in the first part of the course, especially compared to some of the other online courses I’ve taken on deep learning. Going from zero to top 5% of the Nature Conservancy Kaggle competition has been a fun challenge and a great way to apply what we learned in part 1. *If anyone wants to join teams and try to get farther up the ranks, let me know!

I initially learned about this course through hackernews - and then attended the info session in January. My interest in applications of deep learning in wide - but one particular area I’m excited about is using ML to effectively summarize text. I love image classification and computer vision, but there’s something deeply satisfying about teaching a computer to “understand” a piece of text enough to summarize it. Its also a deeply needed skill as we move continue to move into a world of too much information.

I’m passionate about many things including ML, but my two biggest hobbies are kitesurfing and ski touring. In the winter, I ski and in the summer I kite. Any other kiters out there?? The season is coming soon in SF!


Hi, I’m Igor and I’m joining in person in SF.

I studied at Insight Data Science and worked in software related jobs and startups. Between employment phases I founded several startups in mobile applications, advertising, creative coding and video processing.

I am interested in creative applications of neural networks. I followed the first part of the course and very happy to joint the second class in person.


Hi everyone, I’m Grégory from France. I recently got my PhD in algebraic combinatorics (which is considered to be computer science in France and math everywhere else) from university Paris-Est Marne-la-Vallée. I am joining this course as an international fellow (3:30 AM, ouch ^^).

  • How did you learn about the course? I learned about this course in the import AI weekly newsletter. I built a basic presentation of deep learning targeted to people who have never heard of it using the content of part 1 (I will share it in another topic).

  • What are you hoping to do with deep learning? I am currently unemployed and looking for a job in data science and hopefully deep learning. I don’t have a precise idea of what I want to do with deep learning yet, all I know is that I love it. I would also be interested in teaching it in university.

  • What are you passionate about? I am passionate about computer science and math in general. I really enjoy to create video animations based on simple mathematical concepts (fractals, etc.). I am also passionate about hypnosis, which I see as a way to “hack” and interact with the brain in a different fashion. In my free time, I slackline a lot which consists in balancing on a piece of 1 inch webbing.

  • What’s something that not many people know about you? I have Asperger Syndrome, which means that my brain functions a bit differently than most people. Because of this, I tend to have a very unique view on problems that I try to solve and it made me complementary to research teams I worked with during my PhD.


Hi everyone,

I’m Alexis from Québec, Canada. I’m a PhD student in robotics, I am more specifically working on cable-driven parallel robots. You can see a device I have been working on here : .

I learned about part 1 of the course from the reddit/r/python group. and now I’m a international fellow for part 2.

I am now working on biosensor reading and processing in a machine learning project in partnership with the school of psychology at Laval University.

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I’m Jon! I’m a software engineer/data scientist-- the past few years I’ve been doing full-stack web development and some machine learning. I am currently a student in the data science immersive program at Galvanize and am doing the self-driving car Udacity program. I also have taught web development at a coding school and developmental math at a community college. As an undergrad I majored in math and psychology, and minored in physics and economics. I first found my interest in machine learning doing research in a computational neuroscience lab predicting behavioral states with fMRI data. I’m living in San Francisco and am originally from Salt Lake City, Utah.

I found out about the course through some fellow alumni of a previous engineering program I had been a part of.
I’m really interested in computational sustainability and hope to be able to use deep learning for environmental sustainability and for the social good. I’m also quite taken by the use of deep learning to create/ in the arts (e.g. Magenta). In that realm, I’m interested in the poetry and story creation. Strangely coincidentally, like @Even, I have also been thinking about using deep learning to create childrens books. I would also like to explore how deep learning could be applied to outdoor recreation, ways in which technology can help people get outside more and safely as well as working toward keeping nature beautiful and thriving.
Non-coincidentally, the areas I think applying deep learning would be interesting are include the areas I’m passionate about-- utilizing technology for social and environmental good, creative writing, the outdoors, music. I’m also passionate about learning and sharing knowledge. I’m generally not satisfied unless I’m diving deep into learning something new and interesting.
Something people may not know about me is how much I love the art of storytelling in any form. Namely, poetry and technology/data science are the mediums through which I pursue this.

Please feel free to connect with me on LinkedIn!

Unfortunately, I’m out of town for the first class, but am excited beyond belief to get to participate in this course and meet all of you!


I’m guessing you’re familiar with this already - but in case not, @rachel and I absolutely adore these math animations:

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Hi everyone,

My name is David Woo and I work in the analytics team in Instacart. My background is in Operations Research and will be attending this course in person.

How did you find out about the course? Have you used your knowledge from part 1 on any fun projects yet?
I found out about the course while taking part 1 through MOOC and really liked the way how the course was thought.

What are you hoping to do with deep learning?
I’m excited about how deep learning can find structure through unsupervised learning and interested in how deep learning and human intuition can form an symbiotic relationship. As a fun hobby, I’m really excited to try to use deep learning to race a self-driving autonomous toy cars.

What are you passionate about?
I’m passionate about learning about different subjects ( technical and non-technical as well) . I was fortunate to live in a few countries since young: Australia, Singapore, Indonesia and really passionate about travelling and learning about different cultures. Hope to visit Europe soon.

Also, a really big avid squash fan as well! ( the sport and the vegetable!) :slight_smile:

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Where do you play in SF? I used to play quite a bit in Melbourne, but haven’t played at all in SF…

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Hi @alexfcote,

I’m also doing graduate studies in mechanical engineering (Optimization) at Laval University :wink: We could form a Laval team if you want to work through the course together.

Here’s my intro for the thread:

I’m a code reviewer, forum mentor and classroom mentor in Deep Learning, Computer Vision, AI, Self-Driving Cars, Robotics, Natural Language Processing and Speech Recognition at As I mentioned, I currently do research in Optimization. I also have more than 10 years of experience as a Software Engineer. I’m planning to focus my career on deep learning.

Thanks a lot and have a great day, Maxime

Hi everyone! I’m Mariya, joining in person from SF. I’m Head of Research & Design at TOPBOTS, a strategy & research firm focused on bots & AI. We educate and advise executives from Fortune 500 companies like L’Oreal, Unilever, Paypal, etc on how to assess and roll out new technologies at their organizations.

I’m a designer & strategist as well as a writer for Forbes, VentureBeat, and a few other publications. While I did study CS & Math in undergrad, I’ve never worked as a professional engineer or data scientist, so I’ll be hustling to keep up with the rest of you in class!

How did you find out about the course?
I researched reviews of both online and in-person deep learning educational offerings and came across both the Data Institute website as well as the website. The applied learning and diversity focus made this program stand out from the (ever growing!) list of competitors.

What are you hoping to do with deep learning?
There is so much low hanging fruit for applied deep learning. Personally, I’m interested in mitigating the problems of fake news and use of AI as weaponized propaganda.

Professionally, I’m interested in understanding the state-of-the-art in NLP as applied to conversational AI in voice and chatbot applications which we are designing for Fortune 500 brands. AI for social good and humanitarian initiatives is another key area our company wants to contribute to, primarily in an advisory role to non-profits and social entrepreneurs. Examples include the “computational sustainability” work Stefano Ermon’s group does at Stanford and the use of even rudimentary bots like UNICEF’s U-Report to uncover social injustice.

Finally, I want to be able to “translate” arcane scientific papers into accessible and compelling language for non-technical business leaders to understand. What we have discovered in our business is that while AI research is certainly hard, deploying and operationalizing research into enterprise workflows is even harder, requiring large-scale interdisciplinary and interdepartmental coordination led by informed executive leadership. For AI to be successful, we have to communicate successfully.

What are you passionate about?
I love design, which I define as a problem solving approach that combines art, communication, and science. In my design work, I’ve been fortunate to connect with brilliant thinkers in all industries, at all levels, who approach problems in unique ways that can be recombined for even more inspiring solutions.

What’s something that not many people know about you?
I love to dance! I led two dance troupes in college and was a competitive latin ballroom dancer in NYC after graduation. My favorite dance style is a sensual partner dance from Brazil called Zouk which involves unique head movements and styling you don’t see in other dances. If you’re ever feeling overly cerebral or stiff from debugging your deep networks, just ask me to take you dancing for a psychosomatic break :smiley:


Hi Everyone - I’m Brookie. I’m a data scientist at World Resources Institute where I use satellite imagery to detect deforestion. I studied physics and math, and was a string theorist before running off to South America to work in microfinance. But that all seems very very long ago…

  • I found out about the course through a friend (Robin Kraft) who took part one. He loved the course but sadly won’t be joining us for part 2.
  • I wasn’t around for part 1. I’ve been really enjoying going through part 1 online. Sadly I’m still a bit behind, but hopefully I’ll be all caught up by the time our second class rolls around.
  • My work at WRI has all been model based approaches. The hope is that by using deeplearning we’ll be able to create systems that are not only more accurate, but also more detailed. Systems that go beyond simply detecting forest loss, and allow us to identify drivers of loss and perhaps even make predictions.
  • Passions: wife, baby, surfing, food
  • Not so secret secret: I once won a moth story slam

Hi, I’m Kelvin. Currently at IBM Research but historically worked in startups. I’m entrepreneurial and I’m very interested in solving new problems.

  • How did you find out about the course? I read about it a couple times in the past and finally my friend pinged me about it earlier this year – which is when I looked into the course. It’s the rule of 3 in consumer marketing. The first two times you hear about an unknown product, your interest it piqued but not enough to convert. However on the third time you look into it because of the repeated exposure.

  • What are you hoping to do with deep learning? I’m very interested in exploring how reinforcement learning can apply to real world problems.

  • What are you passionate about? I love cooking. I’m more than happy to spend a day cooking a relatively involved recipe (when I have the time). And, as stated above, I love applying new technology to solve problems in a better way.

  • What’s something that not many people know about you? I took an improv course from UCB in New York and thoroughly enjoyed it.


There is so much low hanging fruit for applied deep learning. Personally, I’m interested in mitigating the problems of fake news and use of AI as weaponized propaganda.

Hey @mariya, you should definitely check out:

I just recently came across it and was intending on posting it on the forums. I was thinking maybe we can get a team together for this out of the students. It’s topical and I think it should be very well suited to RNNs.


Agreed. If there’s enough folks in the class interested we could see if one of the organizers can drop by sometime…

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Hi, I am Ljubomir. I have consulting company Clinical Persona which develops machine
learning classifiers to diagnose diseases and predict outcomes (mostly cancer). So far we
developed five clinical tests using “classical” methods like SVM, Random Forest etc. applied
to genomic and clinical data. We would like to improve cancer diagnosis and treatment by
applying deep learning to image+clinical data.

I watched Part 1 online and have been applying the knowledge in DREAM Digital
Mammography challenge:!Synapse:syn4224222/wiki/401743

I live in East Palo Alto (SF Bay Area) and I’m a big tennis fan


On a related note, it might be cool to invite a top Kaggle grandmaster to come give a talk at the Data Institute. A worthy competitor for you, Jeremy :wink:

I’m not sure that we have any other Kaggle grandmasters in SF - no-one comes to mind, anyway…

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