Adjusted R Squared! pic.twitter.com/e4RlHzBPac
— Chris Albon (@chrisalbon) December 4, 2019
Adjusted R Squared! pic.twitter.com/e4RlHzBPac
— Chris Albon (@chrisalbon) December 4, 2019
Enjoying “Full Disclosure: The Perils and Promise of Transparency” - analyzes things like government-mandated car safety stickers to inform consumers/realign corporate + local gov’t incentives - what they call “targeted transparency” measures. pic.twitter.com/FYVfEkq3qx
— Miles Brundage (@Miles_Brundage) December 2, 2019
Another @kaggle competition @fastdotai v2 starter pack is here! 😄🥳🍾
— Radek Osmulski (@radekosmulski) December 1, 2019
In the repo so far:
✅downloading and processing data
✅training two basic models
✅averaging predictions and submitting to Kaggle!https://t.co/0m6BaNk2qT pic.twitter.com/tBu59pMuPX
This is a great and simple example of using Dask's big data collections (arrays in this case) with it's real-time capabilities. https://t.co/ikBoDVGre4
— Dask (@dask_dev) December 1, 2019
Thank you, It should be really useful as according to this paper https://t.co/TnYFcCMRRR , the unsupervised finetuning and layer wise LR , and one-cycle are crucial for BERT performance. They mange to beat ULMFiT on IMDB with BERT-Base!
— Piotr Czapla (@PiotrCzapla) November 29, 2019
Fantastic free ebook on Linear Algebra written by my colleague Stephen Boyd at Stanford. Highly recommended.
— Reza Zadeh (@Reza_Zadeh) November 29, 2019
PDF: https://t.co/Yq0dNI10Ek
Page: https://t.co/XoZjse3dEt
Course: https://t.co/7cSTCZRUjn pic.twitter.com/Jug5VPO9yW
As part of a course on trustworthy ML, I put together a lecture about differential privacy. Slides are available here https://t.co/kMNbXvHwQZ
— Nicolas Papernot (@NicolasPapernot) November 26, 2019
Feedback if you use some of the material is more than welcome! pic.twitter.com/hLUr3oQXtw
You can now build and run ML pipelines in an interactive Colab notebook using TensorFlow Extended (TFX)! 📓
— TensorFlow (@TensorFlow) November 25, 2019
Develop in a notebook and then export to a production-ready ML pipeline.
Try it in your browser today → https://t.co/6T034jsRV3 pic.twitter.com/e9IcEoUJdy
I wrote an 8k word doc on machine learning systems design. This covers:
— Chip Huyen (@chipro) November 24, 2019
1. Project setup
2. Data pipeline
3. Training & debugging
4. Serving
with case studies, resources, and 27 exercises.
This is the 1st draft so feedback is much needed. Thank you!https://t.co/1kByQTV5DC
A list of papers on BERT compression (hat tip to @tianchezhao) https://t.co/WgQjjQPubo
— Leonid Boytsov (@srchvrs) November 24, 2019
Today we also started open-sourcing some of our datasets & NLP example projects!
— Ines Montani 〰️ (@_inesmontani) November 22, 2019
Includes 1k+ annotated examples each, train/eval scripts, results, data vizualizers & some powerful tok2vec weights trained on Reddit to initialize models.
💝 Repo: https://t.co/xHLVaMRc69 https://t.co/8pkn2xz0AG pic.twitter.com/SCfWX2ahby
A model is a story that explains your data.
— Brandon Rohrer (@_brohrer_) November 21, 2019
Here are some guidelines for choosing the best one.https://t.co/YfnCvXnaDy pic.twitter.com/yjGg1pU6bl