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Nov 3 • 8 tweets • 3 min read
We're sharing some of our favorite student projects from this year's cohort.

Read on for a sign language word detector, a semantic image blurring system, and more!
👻 Image Anonymiser: building on object detectors to selectively remove information from images.

Team: Sami Saadaoui, Vladislav Vancak, Lawrence Francis, Dan Howarth, Allan Stevenson

Really nice writeup here: saadaosa.github.io/ImageAnonymise…
BuggingSpace: an interface for "red-teaming" open source text generation models on @huggingface.

Team: Sashank Pisupati, Sajenthan Vigneswaran, Kemp Bray, Jean-Antoine Zagato

We love to see work on better evaluation of models!

Try it out yourself: huggingface.co/spaces/fsdlred…
All these projects have demo videos (and some have more, like slides or a GitHub repo) available on our course website:

fullstackdeeplearning.com/course/2022/pr…
Gesto AI: an ASL-to-English word-level translator.

Team: Shivam Arora, Daniel Firebanks-Quevedo, Pablo Oberhauser, Dhruv Shah, Ibrahim Sherif, Samuel Tang

Try it for yourself here: …-asl-word-recognizer.streamlitapp.com
choosistant: summarize the pros and cons from product reviews.

Team: Kimotho, Murad Khalilov, Nam, Omar Ali Sheikh, Sofiane Chami
Semantic Search & Sentiment Analysis: apply helpful NLP to PDFs in a web app.

Team: Sam Tonetto, Francisco Perez-Sorrosal, Navaneeth Tirupathi, Alexander Chia, Priyam Sadhukhan
You can find more projects and demo videos on our course website:

fullstackdeeplearning.com/course/2022/pr…

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More from @full_stack_dl

Full Stack Deep Learning Profile picture
Nov 3
In past threads, we've seen that our students this year built webcam-based visual Q&A, semantic search engines, and more.

In this final thread, we'll see the last few projects, from an iNaturalist-style plant identifer to a streaming art generator.
In-Browser AI: neural networks in the browser with the @onnxai web runtime.

Team: @visheratin

Check it out here: edge-ai.vercel.app Image
Audio VTuber: animate a "virtual YouTuber" to speak with your voice.

Team: Alex Service, Moon Ma

Try it here: audiovtuber.lol Image
Read 8 tweets
Full Stack Deep Learning Profile picture
Nov 2
We've tweeted a lot of threads on the FSDL 2022 labs, which build a simple demo OCR app from scratch.

It may not have been obvious from the outside, but these labs are designed to each be useful independently.

So we've collected them up into a 🧵 thread-of-threads 🧵.
(Pre)Labs 0-3 🧵: Overview, PyTorch, Lightning + CNNs, and Transformers
Lab 4 🧵: Experiment Management
Read 8 tweets
Full Stack Deep Learning Profile picture
Nov 2
In our last thread, we made it through the first few student projects, including a video summarizer and an X-ray reader.

Now, let's do a few more -- like the nanofiber measurer and the recipe inverter.
Archaeological Feature Detector: automatically detect and interpret evidence for remains of prehistoric structures in LIDAR data.

Team: Philanoe, jmmoreu, kempbray, lakillo (github u/ns)

We love to see interdisciplinary work!
Semantic Search Engine for Images: find images from a database using natural language descriptions.

Team: Sandhya Govindaraju, Utkarsh Vardhan, Gabriella Chiribau, Amit Kumar Sah

Really impressive monitoring setup too. Try it for yourself!

fsdl2022.plotto.info
Read 11 tweets
Full Stack Deep Learning Profile picture
Nov 1
At FSDL, we believe in the power of shipping. So in the class, students build and share their own ML-powered products. This year, folks built all kinds of things, from a recipe inverter to a VTuber generator.

We'll be sharing some of our favorites here on Twitter.
Course Co-Pilot: convert a YT video into a text summary.

Team: @waydegilliam, @kurianbenoy2, @suvash

We look forward to using this to automate lecture note generation! Image
You can find a demo videos (and more!) for Course Co-Pilot and the rest of the projects on our course website:

fullstackdeeplearning.com/course/2022/pr…
Read 10 tweets
Full Stack Deep Learning Profile picture
Oct 31
Over the last few months of running the Full Stack Deep Learning course, we released one lecture video (+notes) each week and wrote an accompanying Twitter thread.

That's a lot of content, so here's a 🧵 thread-of-threads 🧵 collecting all of them up.
Lecture 1 🧵: Course Vision and When to Use ML, by @josh_tobin_
Lecture 2 🧵: Development Infrastructure & Tooling, by @sergeykarayev
Read 11 tweets
Full Stack Deep Learning Profile picture
Oct 26
🧪 Lab 5: Troubleshooting & Testing 🧪 didn't get a thread at time of release, so let's do one now!

For the testing part of the lab, we cover the basic tools with an eye on what works best for ML.

For example, Shellcheck to catch weird edge cases in the bash scripts that often glue ML pipeline steps together and fail silently with dire consequences:

shellcheck.net
We also go over Python linting tools like flake8 and black.

Tools are great, but tool management is not.

We recommend (and in the lab walk you through) using @pre_commit to manage and run your quality assurance tools.
Read 8 tweets

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