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Data Science Weekly Newsletter
December 19, 2019
Key trends from NeurIPS 2019
With 51 workshops, 1428 accepted papers, and 13k attendees, saying that NeurIPS is overwhelming is an understatement. I did my best to summarize the key trends I got from the conference...
Why video games and board games aren’t a good measure of AI intelligence
Beating humans at chess and Go is impressive, yes, but what does it matter if the smartest computer can be out-strategized in general problem-solving by a toddler or a rat? This is a criticism put forward by AI researcher François Chollet, a software engineer at Google and a well-known figure in the machine learning community. Chollet is the creator of Keras, a widely used program for developing neural networks, the backbone of contemporary AI. In this interview, we learn more...
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NeurIPS 2019 featured robot curling players and coffee makers
One particularly active category of research this year was robotics, which saw workshop and paper contributions from Intel, the University of California at Berkeley, and other leaders. Perhaps the most intriguing of these were novel approaches to training a team of machines to jointly solve a problem, and a multi-stage learning technique that uses pixel-level translation of human videos to train robots to complete tasks...
SynSin: End-to-end View Synthesis from a Single Image
Single image view synthesis allows for the generation of new views of a scene given a single input image. This is challenging, as it requires comprehensively understanding the 3D scene from a single image. As a result, current methods typically use multiple images, train on ground-truth depth, or are limited to synthetic data. We propose a novel end-to-end model for this task; it is trained on real images without any ground-truth 3D information...
Famous Fluid Equations Spring a Leak
Mathematicians have suspected for years that under specific circumstances, the Euler equations fail. But they’ve been unable to identify an exact scenario in which this failure occurs. Until now....
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Training & Resources
An Introduction to Neural Information Retrieval
This tutorial introduces basic concepts and intuitions behind neural IR models, and places them in the context of classical non-neural approaches to IR. We begin by introducing fundamental concepts of retrieval and different neural and non-neural approaches to unsupervised learning of vector representations of text. We then review IR methods that employ these pre-trained neural vector representations without learning the IR task end-to-end...
Generative Teaching Networks
This video explores an exciting new Meta Learning paper in which the classifier learns its own training data! This video will explore the application of this to Neural Architecture Search, weight normalization, and the use of curriculum learning!...
Common Voice: A Massively-Multilingual Speech Corpus
The Common Voice corpus is a massively-multilingual collection of transcribed speech intended for speech technology research and development. The most recent release includes 29 languages, and as of November 2019 there are a total of 38 languages collecting data. Over 50,000 individuals have participated so far, resulting in 2,500 hours of collected audio...
Do more with your data!
If you're looking to make your data skills stand out, then be sure to check out Manning's range of books and video courses. They're offering 40% off everything in their catalog, so there's no better time to learn something new...
For a detailed list of books covering Data Science, Machine Learning, AI and associated programming languages check out our resources page . P.S., Enjoy the newsletter? Please forward it to your friends and colleagues - we'd love to have them onboard :) All the best, Hannah & Sebastian