Hi all,
Looking forward to meeting some more of you! I’ve got an EE background, and currently I work on the R&D team at Fitbit. I’m here to get fast, practical exposure to DL to test the waters and see how I’d like it as a potential future path.
I noticed a few other road cyclists further up the thread - one of my favourite things about the city is being able to get out of it on the weekends into some beautiful coastlines and countryside!
T.
Hello everyone!
My name is Sravya, I am a senior software engineer at Cloudera currently working at the intersection of Big data + Security + Open source. I am also the Vice President of the Apache Sentry project, a top level Apache Software Foundation project. ASF is one of the key drivers of open source software. I am also very passionate about using tech/data for social impact and have worked with various non profits and government to leverage data and technology. Some of them include DataKind, California Department of Justice, Thorn.
I started following Jeremy’s work on twitter after his talk at Strata Hadoop - " Data Scientists, you can save lives". And when I heard about this course, I just jumped on it. Both my husband and myself applied and got in, but my lovely husband insisted I take up this course while he takes care of our toddler on Monday nights as he realized my passion for it.
Here are my goals for the course:
While I am not doing tech stuff, I love spending time with my family, dance (Indian classical), play tennis.
It’s so gratifying to hear that a talk I’ve given has helped make a connection - thanks so much for sharing 
…and I met Tim when he interviewed me at Scale Venture Partners: http://tapes.scalevp.com/startups-ai-first-world-jeremy-howard-fastai/
Hi folks,
I’m Jesse, a neuroscientist at UCSF, with a PhD in neuroscience from UCLA. I use neuroimaging to ask questions about the human brain (the ultimate learning machine, for now). In the Memory and Aging center, our goal is to cure dementia, by getting better at diagnosis, disease monitoring, and understanding genetic and environmental risk factors.
I’m interested in deep learning because it’s a revolution in computer vision. It is inspired by the human visual system architecture, and confers machines with human-like visual processing capability. As a side project, I’ve been experimenting with autoencoders on MRI images to build a brain image search engine. I want to dive deeper into this endeavor and peer under the hood of what these models are “seeing” when they identify features that make two images similar or different. This course seemed like the best place to do that! Plus, I know Jeremy’s recent startup Enlitic worked on biomedical images, so who better to learn from?
I love to run, read, meditate, and travel to weird places. Looking forward to getting to know you guys!
Jesse
I’m a bit late to the party (just joined the course a few hours after the class on Monday!) – but very excited to meet you all next week!
I’m a self-taught engineer (with two years of Computer Science from University of Toronto, a degree that I never finished) and I’ve worked on a few data systems at relatively high scale before at Taplytics.
My quant experience has been mostly around Product Management but have recently started to explore the realm of Machine Learning and how it can help NLP.
Excited to learn more and connect with others who are trying to do the same.
Outside of all of this, you’ll find me running alongside the bay in downtown SF or reading sci-fi.
Hi all,
My name is Michael Castelle, I’m currently a Ph.D. candidate in Sociology at the University of Chicago, working on a dissertation that examines the history and sociotechnical development of databases / transaction processing / middleware in the 1970s and 1980s, and the emergence and regulation of digital markets and exchanges (as well as today’s marketplace platforms like Uber and Airbnb).
I’m taking this course because — despite often being a skeptic regarding claims about the revolutionary properties of supposedly new technologies — I’m fascinated by (and legitimately impressed by) how dramatically deep learning has transformed certain subfields of computer vision and AI in recent years. While “good old fashioned AI” has a long history of being critiqued by philosophers and other humanists in their strong assumptions about symbolic-centered reasoning, I think it’s presently an open question as to what form of critique is appropriate to DL methods (in part because they are currently so inaccessible to non-specialists and to those outside Silicon Valley). But in addition, there is an exceptional amount of opportunity for visual and other multimedia artists to creatively use these techniques (as synthesis instead of analysis); and it seems possible that we have just scratched the surface of potential fun/interesting/weird interactive/real-time applications.
I would very much be interested in teaching a course like this one in the future to students without significant conventional training in math/CS, and so my primary goal is to learn enough to pass these techniques (and metaknowledge about these techniques, including their histories and applications) on to others, whether they have professional aspirations in data science fields or not.
When not in class, you’ll probably find me writing at coffee shops, or researching at the Computer History Museum or the Prelinger Library.
mc
Hi, all!
I’m Lin … really excited re starting he class, but am a little humbled after reading all of your bios!
I am a UCLA EE, and have worked the gamut of EE/CS. Did firmware/hardware design on RadarEnvironmentSimulators, wrote SW, worked with SW engineering tools … always on Unix/Linux (I am honestly somewhat Windows-impaired), and took a segue into patent law. Born in Michigan, but always chose to live in California (LA/OC).
For the next day or so, I’m going to be trying to run the homework on a NVidia Ubuntu, so if any of you have any suggestions, let me know.
lin
Hello, everyone! It’s inspiring to read all your intros - I’m excited to learn and work with our wonderfully diverse group here!
My name is Melanie and I’m a student of USF’s Masters in Computer Science Program. While studying biology during my undergrad, I became interested in the application of CS to big data issues in biology and neuroscience. Through this course, I’m hoping to connect with others that share an interest in bio/neuroinformatics, learn the guts of deep learning, and how best to apply it.
Outside of all this fun stuff, you can find me either practicing muay thai and brazilian jiu-jitsu or city-hiking up to the best viewpoints SF has to offer
@jbrown81 I’d love to talk sometime! I’m interested in neuroscience problems as well and have friends at the Allen Institute for Brain Science who may be able to help.
Hello everyone! My name is Brad, I’m a current student in USF’s Master’s in Analytics Program. I’m extremely excited to be in this course, and more importantly to be involved in this community. I’m very curious to see how everyone plans on applying the information learned in this course to their respective fields of interest/expertise.
A little bit about myself, I graduated from USF with a B.S. in Mathematics a little over a year ago. I mostly took courses in pure mathematics, and while I really enjoyed thinking about non-Hausdorff topological spaces I decided I wanted to get into something more applied and current. I was first introduced to data science through a competition I took part in through USF for IBM. My partners and I failed miserably, but it was enough to pique my curiosity in data science and now here I am.
In addition to taking part in this course, I’ll also working with Jeremy and Rachel through MSAN’s Practicum/internship program. Currently several other students and I are helping curate the lectures for online consumption, so if you’d like to help in that venture please let me know! I look forward to meeting all of you throughout the course!
In approaching the answer to your question, I found that I’m unable to reach a single response. - [Bad Topology Joke]
Hi everyone! I have already enjoyed getting to know some of you over the last two weeks. A little bit about me, I work as a recommendations machine learning engineer at Udemy, an online learning company. I think a lot about how to aid discovery of courses relevant to a students learning goals. Around 65% of our 12 million students are outside of the US, with 40% in developing countries. A lot of what I am currently working on is understanding how our discovery algorithms should take into account local tastes, willingness to pay and learning pathways.
More generally I am fascinated by how we can create a good learning experience in an online environment. Creating a community across remote participants is very difficult, but something that is I think is essential for online learning to be a substitute for in person learning. My interest in education runs in the family, my mom is a life long educator in Southern Africa. I grew up mainly in Mozambique (so would love to connect with any Portuguese speakers in the class!). The whole family currently resides in Nairobi, Kenya.
I am taking this course because I am particularly interested in how we can apply deep learning to solve social issues. Three years ago I founded a bay area non-profit called Delta Analytics ( http://www.deltanalytics.org/) that provides pro-bono data science for non-profits across the world. Our fellows are all data scientists, software engineers and analysts in the Bay Area who give their time part-time outside of their full time jobs in 6 month cohorts. It has been an incredible experience working with smart people to fill a skill gap, and I already can’t wait to learn from the passion of like minded people in this class.
Unfortunately, I am one of those people able to commit less than 8 hours of the week to this class so would appreciate your support as I go through the material. Would love to know of any regularly meeting study groups during the week. I am also currently working on a deep learning project as part of UnifyID artificial intelligence fellowship which meets every weekend for the next 7 weeks so hopefully over exposure to deep learning will help accelerate the learning!
Looking forward to meeting more of you through slack, forums and in person!
Hi all,
My name is Ben, I’m originally from Canada and have a PhD in neuroscience, focusing on the psychophysics and brain mechanisms of human memory. After a long windy road I wanted to try something different, something that seemed exciting, and I’m now a data scientist at a company called “Quid”. Quid is a data visualization platform for text, creating a visual and interactive graph network out of news articles, company descriptions, and patents.
I have been really into deep learning for a while now and I think its not long before deep learning is no longer “cool” (as Jeremy says), but required knowledge for continued existence in data science (or least the most interesting parts). Although I love imaging, and am a fairly serious photographer, I’m currently most interested in exploring the limits of how close deep learning can get to really understand text and making intelligent-seeming judgments about text. Needless to say, I am the main deep learning advocate at our company and am trying to get everyone on board (with at least some success :-).
This video is where I first found about your work and this course
Hello everyone, I’m late to the party here. On Saturday I stumbled into Tetiana Ivanova - How to become a Data Scientist in 6 months a hacker’s approach to career planning. Near the beginning of that presentation, Tetiana mentions that she was inspired by this guy called Jeremy Howard. The reference led me to the TED Talk which lead me to Enlitic. The following Monday I’m able to drive from my home in the redwoods here in Bonny Doon to San Francisco for the class. The Bay Area never ceases to amaze me! Thank you all for having me.
Two years ago I quit my job at a hedge fund to start Nourish Balance Thrive, a small functional medicine practice for athletes. I have two undergraduate degrees, one in electronics and one in computer science. I work with two medical doctors, one of whom is like me, a pro mountain biker, and the other is a biochemist and Ph.D. fellow. I employ a registered nurse and I work with my wife, a food scientist.
Over the past two years we’ve collected detailed health assessment questionnaires, blood chemistry, urinary organic acids, stool culturomics, and hormone data from around 800 athletes, many of whom are elite or even world-class. Our primary concern is fixing broken humans, second is performance. After a steep onramp, we’re now shifting away from exploration and into exploitation mode.
My burning question is: “can we automate the individualised diet and lifestyle medicine that we do?”
Second, I’m interesting in exploring our data for phenotypes. For example, we see lots of masters endurance athletes with iron overload. What other markers accompany that phenotype? Could something in a cheap test, e.g. heart rate variability or a complete blood count (CBC) be predictive?
Third, I’d like to build a learner capable of interpreting the scientific literature and use that knowledge to make diet and lifestyle recommendations. Do a Pubmed search for “diabetes or obesity”. Limit the result to the past 5 years. Last time I checked, that yielded 243k results. Who has time to comprehend all that? Only a machine.
I’m wondering if some of medical applications I’ve seen thus far are digging in the wrong place, so to speak. I’m less interested in drug discovery and early detection and more interested in creating systems that elicit the behaviour that prevent the problem in the first place. If you’re interested in what that behaviour might look like, I’d recommend my co-founder’s talk, A Systems Analysis to Insulin Resistance.
I’ve completed 60% of online training course called Creative Applications of Deep Learning with TensorFlow. It’s beautifully done and only $10 a month!
I’ve not been this excited about technology since the Internet, and I can’t wait to learn about your chosen applications.
MOOC students - we’d love to hear from you too! Please tell us a bit about yourself, and what you’re hoping to get from this course.
I’m Pavan Ravipati!First off, I love that Markdown is supported in the comments and also in .ipynb notebooks–who knew! I work at GitHub as a solutions engineer (which means I solve setup / configuration / and integration problems for customers who are looking to buy GitHub Enterprise). In the day-to-day of my job I write very little code, which I miss, as I previously worked as a full stack developer.
I’m interested in learning about deep learning because I think there is a tremendous opportunity for aspiring entrepreneurs who understand the subject well to shake up existing industries. I feel inspired by the bios of the other people in the class–it’s clear to me that many of you are experts in your respective fields! While I am by no means an expert, I intend on contributing by working hard to learn the material and be helpful to my peers in any way that I can.
Cheers! 
