Deep reinforcement learning for supply chain and price optimization https://t.co/fMBah2H5zp#AI #DeepLearning #MachineLearning #DataScience pic.twitter.com/47PjQT4hPI
— Mike Tamir, PhD (@MikeTamir) March 24, 2020
Deep reinforcement learning for supply chain and price optimization https://t.co/fMBah2H5zp#AI #DeepLearning #MachineLearning #DataScience pic.twitter.com/47PjQT4hPI
— Mike Tamir, PhD (@MikeTamir) March 24, 2020
I’m really excited about this new intro to D3! It takes a very different tack than previous tutorials, and I hope it helps more people get into visualization. https://t.co/Ic7jvNFFwF
— Mike Bostock (@mbostock) March 24, 2020
In this new technical blog, learn how to use @NVIDIA #GPU libraries and #Python to achieve the state-of-the-art performance in the domain of exotic option pricing in #finance.https://t.co/PHTqN06rLG
— NVIDIA AI Developer (@NVIDIAAIDev) March 24, 2020
"Machine Learning code generally doesn’t throw errors, it just underperforms" https://t.co/zIF0JhYb4j pic.twitter.com/zJIxPFxtJe
— Peter Skomoroch (@peteskomoroch) March 24, 2020
New mnemonic essay: "Quantum Mechanics Distilled": https://t.co/iayV16wKCV Explains in depth the fundamental ideas of quantum mechanics. Joint with @andy_matuschak
— michael_nielsen (@michael_nielsen) March 23, 2020
Happy to find that the CS224w lecture on "Limitations of Graph Neural Networks" is out on YouTube. I helped making the slides :) The entire course is highly recommended for folks interested in graph ML!
— Weihua Hu (@weihua916) March 23, 2020
Video: https://t.co/ytrathJudP
Course website: https://t.co/Qu4QIOhQjt
Nice resource from the great @minebocek to aggregate #rstats analyses and representations of Covid-19 https://t.co/oj03udzHzv pic.twitter.com/OnibH4UR4B
— Michael Lopez (@StatsbyLopez) March 23, 2020
The https://t.co/ktYtgB7WOT data ethics course will be released this summer, although I want to share one video early:
— Rachel Thomas (@math_rachel) March 20, 2020
Disinformation (covering disinfo+coronavirus for first 30 mins)
Watch it here: https://t.co/FSWjHDjkHy 1/ pic.twitter.com/LaYC81axaI
Kaggler @JohnMillerTX has helpful guidance, along with a template for creating analytics reports, for anyone looking to get started with our COVID-19 Open Research Dataset Challenge || https://t.co/t3jG1adfsc
— Kaggle (@kaggle) March 20, 2020
Two more @rapidsai @kaggle kernels that showcase the incredible speedups for tSNE and UMAP dimensionality reduction algorithms on GPUs.
— Bojan Tunguz (@tunguz) March 20, 2020
Kannada MNIST: https://t.co/9jbNv1KzD7
Fashion MNIST:https://t.co/IId2Tw6uVV
It takes Rapids seconds what often takes hours on CPU. pic.twitter.com/hZnZ51wuIk
Overfit Vs Underfit https://t.co/eZ2bbpDzwV pic.twitter.com/I4O1WYO7ku
— Chris Albon (@chrisalbon) March 20, 2020
This is a well-written overview! Also, for a higher-level, more philosophical take on search in generation models see my recent class (slides: https://t.co/RLQiF5dLqv, video: https://t.co/nSpREeMOG1)
— Graham Neubig (@gneubig) March 19, 2020
I discuss the relationship between model, search, and output quality. https://t.co/i7OWXB3Mfo pic.twitter.com/BB0jaKAVyE