The 99%, an amazing new GAN and noble Lithium – Friday Faves

This week in the Friday Faves we have a cheeky Tweet, a new GAN that might find some amazing applications in geophysics and the Nobel Prize for chemistry.

99%

Let’s start out with some wisdom of the creator of Keras:

If your classifier is "99% accurate", either you're using the wrong metric (a metric this high is not informative), or you have an overfitting or leakage problem.

Metrics are feedback points on the way towards better models. Not trophies to show off. They should be actionable.
— François Chollet (@fchollet) September 24, 2019

VSCode Universe now with Jupyter

My editor of choice is VSCode with the Python extension. Finally, they added native Jupyter support. Read the announcement and then some.

WaveForms in a GAN

In a story that only the internet can write, Marco Pasini experimented with an unpaired GAN architecture. This has been done by pix2pix before, which brought me many hackathon projects and even the first application of GANs in geophysics ever. But those are image-based. MelGAN is waveform-based on different length tracks!

I love the idea of combining siamese networks to stabilize the unpaired training, but read for yourself, because the reddit community /r/machinelearning encouraged Pasini to write up a paper!

Considering the trajectory of pix2pix, which led us to almost believable DeepFakes today, it will be exciting to see what this brings in the future. Moreover, waveform transfer may solve some technological problems of matching vintage oil-based streamer seismic data sets to modern 4D acquisitions.

Pytorch 1.3 is out!

On another note. Just a week after Tensorflow 2.0 finally dropped their big revamp, PyTorch bring mobile support, quantization and other nice tools for researchers.

PyTorch 1.3 includes support for model deployment to mobile devices, quantization, & front-end improvements, like the ability to name tensors. New tools & libraries are also launching for improved model interpretability & multimodal development. Read more: https://t.co/HaaIt9cU3U pic.twitter.com/UKHdU7sHIH
— Facebook AI (@facebookai) October 10, 2019

Nobel Prize goes to Lithium Batteries

You’re probably reading this on a device run by Lithium-Ion batteries. Why does this warrant a Nobel Prize? If you get it wrong you get the Samsung Galaxy Note 7. If you get it right, you get computers more powerful than the machines that sent humanity to the moon, in our pockets running for a day and charging in below an hour.

In a world that is slowly edging toward de-carbonization, energy storage is the biggest problem to tackle. Learn something about Lithium batteries and take a look inside:

Chemists reacting to announcement of Nobel Prize

Nobel Press Release: “They created a rechargeable world“

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Jesper Dramsch

PhD Student at The Way of the Geophysicist

... is a geophysicist by heart. He works at the intersection of machine learning and geoscience. He is the founder of The Way of the Geophysicist and a deep learning enthusiast. Writing mostly about computational geoscience and interesting bits and pieces relevant to post-grad life.

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