Predicting future medical diagnoses with RNNs using Fast AI API from scratch
— Rachel Thomas (@math_rachel) April 22, 2019
(full pytorch implementation of Doctor AI paper using Electronic Health Records) by @SparklePuleri https://t.co/a2wmQEQ7Cg
Predicting future medical diagnoses with RNNs using Fast AI API from scratch
— Rachel Thomas (@math_rachel) April 22, 2019
(full pytorch implementation of Doctor AI paper using Electronic Health Records) by @SparklePuleri https://t.co/a2wmQEQ7Cg
Swift + TensorFlow is a next-generation platform for machine learning that incorporates differentiable programming. In this notebook a go over its basics and also how to create a simple NN and CNN.
— Zaid زيد (@zaidalyafeai) April 22, 2019
Notebook: https://t.co/f9rD5IQSHW
ICYMI 👩🏫 Great tutorial through the forecasting pipeline…
— Mara Averick (@dataandme) April 17, 2019
📈 "ForecastFramework Demo" by @reichlabhttps://t.co/RcJIfUQF0X #rstats pic.twitter.com/BRn2lXnnjn
A great GitHub repository with tutorials on getting started with PyTorch and TorchText for sentiment analysis in Jupyter Notebooks. What a great resource! https://t.co/XSoUIwwYJ8
— Sebastian Raschka (@rasbt) April 16, 2019
🛠 Step-by-step to automate that pipeline w/ GitLab…
— Mara Averick (@dataandme) April 14, 2019
"How to easily automate R analysis, modeling and development work using CI/CD, w/ working examples" 👷♂️ @jozefhajnalahttps://t.co/3XVAGa9W0t #rstats pic.twitter.com/PDX5Yd20aY
We just added a new tutorial that shows how to write Transformer (‘attention’ is all you need) in #TensorFlow 2.0 from scratch.
— TensorFlow (@TensorFlow) April 11, 2019
Check it out here → https://t.co/6dmNGC9NkE pic.twitter.com/GY1LwdxySK
In this week's #tidytuesday screencast, I analyze winners of Grand Slam tennis tournaments since 1968 🎾
— David Robinson (@drob) April 9, 2019
Don't miss my attempt to predict tournament winners based on a player's average past performance https://t.co/VIi3HUgOLD #rstats pic.twitter.com/V0uENpuL8w
🃏 Double-down when you're down-and-out?
— Mara Averick (@dataandme) April 8, 2019
"Martingale strategies don't work, but we knew that - Simulation analysis in R" 🖋 @danoehm https://t.co/ZIMn7JD79y #rstats pic.twitter.com/VGAhWlBWfg
Colab notebook from my Causal Inference practical at MLSS2019.
— Ferenc Huszár🇪🇺 (@fhuszar) April 8, 2019
Illustrates generative processes, interventions and counterfactuals through structural equation models.
You can make a copy and play around with it.https://t.co/9AVNyJuWrL pic.twitter.com/AuGyD9pU7u
Getting your Matplotlib plot framed, laid out, and axis-ed up just right can be a giant pain in the butt. Here are all my best tricks.https://t.co/HKuqQNsufl
— Brandon Rohrer (@_brohrer_) April 8, 2019
Framing plots in @matplotlib is the latest addition to @E2eMl Building Blocks. pic.twitter.com/NKmMuNSYky
Guide to Coding a Custom Convolutional Neural Network in TensorFlow Core https://t.co/cu4QrGVV1H #AI #DeepLearning #MachineLearning #DataScience pic.twitter.com/ZOKlsNU2FQ
— Mike Tamir, PhD (@MikeTamir) March 31, 2019
New blogpost: Integrating CUDA-accelerated machine learning algorithms in @rapidsai with hyper-parameter optimization from @scikit_learn and @dask_dev
— Dask (@dask_dev) March 28, 2019
Summary: Things work! But we have work to do for performance.https://t.co/MLvQ1rPmPw
Work by @quasiben