Deep Learning Papers Reading Roadmap
If you are a newcomer to the Deep Learning area, the first question you may have is "Which paper should I start reading from?" Here is a reading roadmap of Deep Learning papers!...
Quick thoughts on GPT3
I wrote up some quick thoughts on GPT3 and tried to do a bit of an explainer for non-technical folks ... 30 years ago, Steve Jobs described computers as “bicycles for the mind.” I’d argue that, even in its current form, GPT3 is “a racecar for the mind.”...
What’s shaking? Earthquake detection with submarine cables
Is it possible to detect earthquakes with submarine cables? We think it might be. A recent experiment using one of our subsea fiber optic cables showed that it could be useful for earthquake and tsunami warning systems around the globe...
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Data Science Articles & Videos
Hiding In Plain Sight: Deep Steganography
Steganography is the technique of covering secret data within a regular, non-secret, file, or message to avoid detection. The secret data is then extracted at its destination. In this report, a full-sized color image is hidden inside another image (called cover image) with minimal appearance changes by utilizing deep convolutional neural networks. We will then combine the hiding network with a "reveal" network to extract the secret image from the generated image...
Machine Learning for a Better Developer Experience
Imagine having to go through 2.5GB of log entries from a failed software build — 3 million lines — to search for a bug or a regression that happened on line 1M. It’s probably not even doable manually! Our solution produces 20,000 candidate lines in 20 min of computing — and thanks to the magic of open source, it’s only about a hundred lines of Python code...
Exploring Faster Screening with Fewer Tests via Bayesian Group Testing
We present an approach to group testing that can operate in a noisy setting (i.e., where tests can be mistaken) to decide adaptively by looking at past results which groups to test next, with the goal to converge on a reliable detection as quickly, and with as few tests, as possible... this approach is particularly well suited for situations that require large numbers of tests to be conducted with limited resources, as may be the case for pandemics, such as that corresponding to the spread of COVID-19...
High-performance self-supervised image classification with contrastive clustering
We’ve developed a new technique for self-supervised training of convolutional networks commonly used for image classification and other computer vision tasks. Our method now surpasses supervised approaches on most transfer tasks, and, when compared with previous self-supervised methods, models can be trained much more quickly to achieve high performance...
Deep learning to translate between programming languages
We’ve developed TransCoder, the first self-supervised neural transcompiler system for migrating code between programming languages. Transcoder can translate code from Python to C++, for example, and it outperforms rule-based translation programs...
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Senior Data Scientist - Grubhub - NY / Chicago
Grubhub is looking for a data scientist to join the Pricing team. As a part of Pricing, you’ll be a member of a small team of data scientists and engineers who shape and optimize how we charge our diners, shaping hundreds of millions in revenue annually. You will work closely both with financial stakeholders as well as engineers to ship models that make Grubhub more efficient with the way in which it charges customers. You’ll construct models and A/B tests as well as write code to improve our modeling capabilities...
Training & Resources
DeepMind: Learning Resources
Below, you’ll find some of the resources we’ve created to help people at different stages of their learning journey to find out more about AI...
Object Detection with RetinaNet
Want to build and train your own object detection model? Here's a high-quality, super readable code example that does it from scratch in under 500 lines of code...
Single Image Super Resolution, EDSR, SRGAN, SRFeat, RCAN, ESRGAN and ERCA (ours) benchmark comparison
This is a keras implementation of single super resolution algorithms: EDSR, SRGAN, SRFeat, RCAN, ESRGAN and ERCA (ours). This project aims to improve the performace of the baseline (SRFeat). To run this project you need to setup the environment, download the dataset, run script to process data, and then you can train and test the network models. I will show you step by step to run this project and i hope it is clear enough...
Seven Databases in Seven Weeks:
A Guide to Modern Databases and the NoSQL Movement
"A book that tries to cover multiple database is a risky endeavor, a book that also provides hands on on each is even riskier but if implemented well leads to a great package. I loved the specific exercises the authors covered. A must read for all big data architects who don’t shy away from coding..."...
For a detailed list of books covering Data Science, Machine Learning, AI and associated programming languages check out our resources page
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