All of the available winners' solution from our ML competitions in one meta-kernel! Thank you @sudalairajkumar https://t.co/w5Y47Xd7sA
— Kaggle (@kaggle) July 12, 2018
All of the available winners' solution from our ML competitions in one meta-kernel! Thank you @sudalairajkumar https://t.co/w5Y47Xd7sA
— Kaggle (@kaggle) July 12, 2018
Today we’re launching Seedbank, a place to discover interactive ML examples which you can run from your browser, no set-up required. Each example can be edited, extended, and adapted into your own project.
— TensorFlow (@TensorFlow) July 12, 2018
Read @mtyka's post for more info ↓ https://t.co/k1McWSm8PG
Another great resource from @AmeliaMN: explaining and visualizing / contextualizing histograms https://t.co/NkTZZBdYxE #icots10 #rstats
— Hilary Parker (@hspter) July 12, 2018
Thank you to everyone who listened in on my #SciPy2018 talk! Sorry I didn't make it through the "Future of AutoML" portion - that live demo took longer than expected. I've posted my slides online here: https://t.co/SW5Q5BS9Fn
— Randy Olson (@randal_olson) July 11, 2018
Excellent post on generalisation of Bayesian methods by @sebnowozin Do Bayesian Overfit? (you would never guess the answer;))https://t.co/9CEpDShhSk
— Ferenc Huszár🇪🇺 (@fhuszar) July 11, 2018
Little Python command-line trick that I use very often:
— Jake VanderPlas (@jakevdp) July 10, 2018
$ python -m http.server
Launch a simple filesystem-backed webserver in one line!
Check out this end-to-end example of generating Shakespeare-like text using tf.keras + eager → https://t.co/icSa6DQ0GQ pic.twitter.com/aixjtlHtTS
— TensorFlow (@TensorFlow) July 10, 2018
Edward - Probabilistic Modeling Made Easy by Maja Rudolph - https://t.co/yQkReJomvU. An introducion to the tensorflow & Edward basics that are necessary to look at a few modeling examples. Examples cover how to fit a Bayesian neural network & an embedding model to real data.
— Python Software (@ThePSF) July 10, 2018
As someone who asks for odds ratios *and* relative risk at the vet 🐶, I 🖤 this post…
— Mara Averick (@dataandme) July 10, 2018
"How the odds ratio confounds: a brief study in a few colorful figures" by Keith Goldfeld https://t.co/d3Y0wbmxmE #rstats #statistics pic.twitter.com/WOdbmeJxBb
TIL Jupyter Notebooks can now share kernels within Jupyter Lab. Makes it easy to share state between notebooks. #Python #SciPy2018https://t.co/EL1yHIhbXP pic.twitter.com/zERcRSBA9e
— Randy Olson (@randal_olson) July 10, 2018
Model AI Assignments https://t.co/twAot7jT4J - This is a neat collection of assignments/exercises to learn about various Machine Learning techniques
— Denny Britz (@dennybritz) July 10, 2018
Btw - this isn't a static, manually curated list. It's generated in Python on kernel tags, votes, and other metadata in a public Kaggle data release (updated daily). Expand the code sections in the notebook to see the source https://t.co/UEajPWWYVu
— Ben Hamner (@benhamner) July 10, 2018