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
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
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
In this #rstats screencast, I parse and explore the COVID-19 Open Research Dataset Challenge (CORD-19) hosted by @kaggle. Includes:
— David Robinson (@drob) March 18, 2020
* tidyr's hoist + unnest_wider for rectangling JSON data
* tidytext + spacyr + scispacy for named entity recognitionhttps://t.co/674sACxPZZ pic.twitter.com/jkaMp7X4MX
📝 "How to create decorators in R" by Andrew Treadway https://t.co/P1RMWt68qx
— Mara Averick (@dataandme) March 17, 2020
📦 "{tinsel}: Decorating functions in R" by @ntweetor https://t.co/OCllKqT6zy
🍔 Decorate your #rstats functions, or don't. Eat Arby's. (my version starring @nihilist_arbys) pic.twitter.com/Q5bEeOXB90
New tutorial!🚀
— Adrian Rosebrock (@PyImageSearch) March 16, 2020
Today I'm sharing a deeply personal post on Detecting #COVID19 in X-ray images with #Keras and #TensorFlow 2.0:https://t.co/ebHezQ38Y3 👍#DeepLearning #Python #MachineLearning #ArtificialIntelligence #DataScience
See rest of thread below for more info 1/11 pic.twitter.com/IDch6HpNRH
📊 Code through *and* a new data pkg!
— Mara Averick (@dataandme) March 16, 2020
🗳 "U.S. Census Counts Data" by @kjhealyhttps://t.co/wLcw9cZnh3 #rstats #dataviz pic.twitter.com/LUUrwBHroY
Common pitfalls in interpretation of coefficients of linear models in machine learning:
— Gael Varoquaux (@GaelVaroquaux) March 13, 2020
A new tutorial in @scikit_learn, by @marmochia, with huge effort on being didactic, based on the example of predict wageshttps://t.co/aPp57AHSKC pic.twitter.com/b5BvW8kcIN
What's even more awesome is that @AllenDowney has made the chapters available as runnable colab notebooks, which is just about the best way to learn I know of!https://t.co/vjM9x0AU24
— Jeremy Howard (@jeremyphoward) March 12, 2020
PyTorch + Cloud TPU + Colab: a set of code pointers and notebooks to get you started. https://t.co/HhG8vX3Q2R
— PyTorch (@PyTorch) March 10, 2020
From PyTorch to JAX: towards neural net frameworks that purify stateful code: https://t.co/Sg3k4XpzTD
— Denny Britz (@dennybritz) March 10, 2020
Great writeup from @sjmielke on how to think about JAX programs and how it all works - from scratch.
✨Very nice work out of @MSFTResearchCAM:
— 👩💻 DynamicWebPaige (@DynamicWebPaige) March 9, 2020
"Graph Neural Networks in @TensorFlow 2.x: Implementation and example training scripts of various flavors of graph neural network in TensorFlow 2.0.
Much of it is based on the code in the tf-gnn-samples (https://t.co/Te12y9CJCi) repo." https://t.co/mXEt8RWpxm