Hi everyone, I’m a student at Make School that’s attended 2 courses with FastAI now. I made keychains last class on a whim to celebrate the first Swift for Tensorflow class. I plan on making some different ones this time around when the inspiration strikes. Would love to hand them out in person but maybe I’ll just make the files available online this time around
As a deep learning practitioner I’ve learned so much with the community around FastAI and want to give back. If anyone needs help with deployments I’ve got experience deploying and training models with my specialty being computer vision. Everything from making a Flask/Django API to Serverless endpoints to iOS. Just hit me up!
My name is Andrew Bibian (@bibisan) and I’m a data scientist/analyst. I got introduced to deep learning through v3 of the MOOC and it’s blown my mind. I’ve since come up with some ideas about to how leverage deep learning to help kids learn to read (removing all screens from the process and using an edge device, a camera, and some API’s… to start).
Overall I’m looking to learn as much as I can to help me better understand how to build/effectively create deep learning models for social good and collaborate with like minded people.
I’m looking forward to learning with everyone! And thanks so much to USF for the diversity fellowship!
Also here’s my blog, https://bibsian.github.io/, which doesn’t have much up there now but it’s where I’m planning to write up course notes and show progress toward my long term goals.
My name is Jason. I work at a VC firm and have lots of interests like sports, designing outdoor gear, 3D designing and printing, parenting teenagers, game design, sewing and traveling (but not now!). My profession is corporate and financial communications, so I’m also interested in that.
I have lots of ideas on how to use deep learning. What I’ve read about prediction tools and sentiment analysis sounds very interesting and I hope to be able to create those tools by the end of this class.
Hi everyone, my name is Nishant Hegde and I work as an Analytics Engineer/Analyst at Netlfix. I’m based in Los Angeles. I’m looking forward to diving into this world with all of you. (LinkedIn)
Hey everyone, my name is Matthew and I am currently a Data Scientist at Reddit! My personal interests include esports, traditional sports, and anything that has a competitive edge to it. I have heard so much about this course and am very excited to apply everything I learn here to my professional career and personal projects. Feel free to reach out if you want to do a joint project together
Hello World! I’m Vivek Rudrapatna – I’m a gastroenterologist at the University of California San Francisco and a budding clinical data scientist hoping to incorporate DL into my research involving electronic health records, clinical trials data and ‘real-world evidence’ esp as it applies to GI and Inflammatory Bowel Disease. Looking forward to interact with and learn from you all!
I’m Yann François, a French engineer based in Phnom Penh, Cambodia.
I’ve been following the last version of FastAI part 1 which I enjoyed very much even if I could not participate much in the past. I really appreciate the work done by Jeremy, Sylvain and all the community to make Deep Learning accessible to everyone. So, when I saw that the Deep Learning certificate would be entirely online, I decided to open my wallet and registered!
Since the last version of FastAi part 1, I left my job working in an environmental NGO to create a company called Forests.AI - https://www.forests.ai/.
My goal is to develop a comprehensive range of solutions using Deep Learning to support Sustainable Forest Landscapes management.
Some examples of projects I’m currently working on:
Deforestation and forest degradation monitoring using both satellite imagery and field cameras
Biodiversity monitoring using ecoacoustic and camera traps
Wood species identification to ensure sustainability wood supply-chain
Mapping of sites suitable for various tree plantation and monitoring of tree survival
Hi, I’m Alvin and currently an automation engineer @ Nvidia. I am located in the Bay Area. My 6-month old son is keeping me busy nowadays. Taking this course from personal interest and hopefully leverage it for work as well.
Hey, I am a computer security researcher. I’m in the Bay Area but I work remote for a company on the east coast. I know a lot of about low level security, program analysis, vulnerabilities. I think this stuff will be cool to apply to those areas. My twitter is @cyberingcc.
For what I would like to get out of this course -
Embeddings - How to create them for non-image and non-language data. Say for, perhaps, assembly code, structural data extracted from files, structures recovered from memory dumps.
Similarity - Once we have embeddings for arbitrary data, how do we do similarity metrics? Minhash? Simhash? Graph database?
Object detection - I understand classification, but how does detection work? Is it just brute force classification over regions of the image? What are the best practices for labeling data?
Segmentation - Similarly, what are the best practices for labeling data for segmentation.
Speed limits - How fast can we classify images, recognize, segment images / frames on say, an embedded device, a device with a TPU (e.g. Google Coral), and on a device with a GPU? Is it more practical to burn the bandwidth and upload data to be processed on a server? Can we do some light processing on the edge?
Arbitrary data translation - Neural translation is very cool, I do not really work with spoken languages though. How would I adapt these models to work on arbitrary data? For example, if I had as input some file bytes, and knew what memory objects they created, could I set up something that translated file bytes -> memory objects?
Satellite Imagery - This looks like good business for a government contractor, so I’d like to be aware of how methods change for the different formats (visible, IR, those weird images with multiple layers).
I am very excited for the course and look forward to using the skills on new research projects.
I am Srinivasa, I am DSP Engineer spent most of my time on audio, I have been interested in Machine Learning and Deep Learning for a long time now but haven’t done much. I’d like to use Deep Learning to solve problems in Audio/Speech. Looking forward to interact and learn from you all.
Hello!
My birth name is Clemente but call me Tino!
I’m an Italian R&D Senior Software Engineer at Deltatre Innovation Lab where we develop cutting-edge prototypes. So, I made science and technology my passion other than my job .
I follow many communities and I am often actively involved as an international speaker and event organizer. Awarded Microsoft MVP and Intel Software Innovator focusing on emerging technologies, Augmented Reality, Mixed Reality, Volumetric Videos, Computer Vision, Machine Learning…
What else to add… I can’t wait to start this course and to learn more!
My name is John A Rowand from the Seattle area and my hobbies include psychology and esports.
My background is in mathematics, analytics, and software engineering.
I’ve completed fastai course v3 and now I’m working on freelance ML projects and studying full time.
I’m so excited to jump into this fastai v4 journey with you all!
My name is Rahul. I am a software engineer at Google; and I work on the Android Framework.
My journey to deep learning began while working on a 20% project with Verily on detecting dry-eye syndrome using Deep Learning.
One of my hobbies is to also build self-driving RC cars that drive themselves around a track using a combination of Computer Vision and Deep Learning.
I thought this class would be a nice formal introduction to Deep Learning. Excited to e-meet everyone & I hope we get to work on interesting problems together.
Hey Folks!
I am Pinaki. I work for Roku. Super excited to meet all of you and explore the latest and greatest in deep learning. I have worked on multimedia information retrieval. I am excited about recommendation systems and DL applications in CF.
My name is Dan and I am a data infrastructure engineer at GitHub. Watching the fast.ai videos from 2019 has been a transformative experience for me. I have learned so much already and I am so excited to be a part of this year’s class!
I’ve been working with my sister in her capacity at NOAA to develop a model to identify Damselfish calls in 26 months of continuous hydrophone recordings and hoping to develop that into a paper for submission by the end of this year.
Thanks to the hard work of the authors of the fast.ai library, and the fast.ai audio library, I’ve been able to train and deploy a model for inference over most of the data, with the rest to be completed soon.
Can’t wait to meet more of you and hear more about what you’ve been working on!
Hey Dave,
I’d be glad to collaborate on the geospatial module. In fact, I work at a geospatial company (Cape Analytics) and I am doing geospatial stuff all the time, including deep learning with fastai.
I have developed an entire library of geospatial things that we are about to open source. Let’s chat in the common area (as soon as I understand how it works, I’m new to the fastai forum )
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.
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