Here's the nbviewer link, since github has so much trouble rendering notebooks (including this one!)https://t.co/lSxdNtsQb7
— Jeremy Howard (@jeremyphoward) October 22, 2019
Here's the nbviewer link, since github has so much trouble rendering notebooks (including this one!)https://t.co/lSxdNtsQb7
— Jeremy Howard (@jeremyphoward) October 22, 2019
Every step of the visualization process changes with real data. Save yourself some grief and get the good stuff in the beginning. The Interactions Lab talks about their experiences https://t.co/hSnTRiBuoW
— Nathan Yau (@flowingdata) October 22, 2019
In this #tidytuesday screencast, I analyze a dataset of horror movie ratings, and use lasso regression to predict ratings based on genre, cast, and plot.
— David Robinson (@drob) October 22, 2019
What's 😱👍: Indian, animated, and drama films
What's 🙄👎: Sharks and Eric Robertshttps://t.co/3qj7NoA4Pf #rstats 🧛♂️👻 pic.twitter.com/OBI6x1O2zX
I made a notebook with examples of cool Python features that either took me a long time to find out or were too intimidating for me to use.
— Chip Huyen (@chipro) October 22, 2019
I especially focus on the features I find useful for machine learning.https://t.co/7LBmu4UuwS
Want to learn how to run Asynchronous Federated Learning in @PyTorch over #websockets?
— OpenMined (@openminedorg) October 22, 2019
This blogpost tutorial by #SilviaGandy shows how you can use #PySyft's WebSocketWorkers to do just that!#100DaysOfMLCode #privacyhttps://t.co/d1roRjpdDr pic.twitter.com/KW9mltEGYC
Causal inference basics in just two slides, @eliasbareinboim initiates #INFORMS2019 attendees in CI pic.twitter.com/tsVikH5W71
— Zachary Lipton (@zacharylipton) October 21, 2019
The Illustrated GPT-2 (Visualizing Transformer Language Models) https://t.co/85cGvGRwb9
— Nando de Freitas (@NandoDF) October 20, 2019
Fantastic piece by @math_rachel on the misuse of metrics in organizations, particularly ones that measure success by number of clicks and time spent on platform. https://t.co/H4gDs2scD1
— Vicki Boykis (@vboykis) October 19, 2019
🐍📰 What does Python 3.8 bring to the table? Learn about some of the biggest changes and see you how you can best make use of them https://t.co/8NLDNwNMDW
— PyCoder’s Weekly (@pycoders) October 19, 2019
Sorry for adding more onto your todo list, but there's also "Algebra, Topology, Differential Calculus, and Optimization Theory For Computer Science and Machine Learning" by Jean Gallier (should be a short read ;) ). Free PDF available from Jean's website: https://t.co/b5Mzpw9b7N https://t.co/RT7jTXs8Le
— Sebastian Raschka (@rasbt) October 19, 2019
Watch "Getting it right: Writing reliable and maintainable R code" with @ajmcoqui from rstudio::conf(2019)
— RStudio (@rstudio) October 19, 2019
🎦 https://t.co/6Uc3GHDG4T
Learn more about and register for rstudio::conf(2020) in San Francisco at https://t.co/rYJqkzCywm #rstudioconf #rstats #DataScience
Mathematics for Machine Learning. Looks like a nice companion book for machine learning and deep learning textbooks. They have a free PDF version on their website, which is nice :) https://t.co/NBFMhcg01s
— Sebastian Raschka (@rasbt) October 18, 2019