Debugging “Software 2.0” requires a bit more effort. How to unit test machine learning code: https://t.co/Hnq4TjYiq2 pic.twitter.com/pff03X9GcO
— hardmaru (@hardmaru) June 27, 2018
Debugging “Software 2.0” requires a bit more effort. How to unit test machine learning code: https://t.co/Hnq4TjYiq2 pic.twitter.com/pff03X9GcO
— hardmaru (@hardmaru) June 27, 2018
Reproducibility tip of the day: If you're sharing research code & data, I'd recommend splitting it into three parts.
— Rachael Tatman (@rctatman) June 27, 2018
1. Raw data & preprocessing code
2. Preprocessed data & modelling code
3. Trained model & evaluation code
The Illustrated Transformerhttps://t.co/t8neQG650S
— Jay Alammar جهاد العمار (@jalammar) June 27, 2018
New blog post visually explaining the model from "Attention is All You Need". It achieved state-of-the-art in some machine translation tasks in 2017. It is also the reference model for coding for Google's Cloud TPUs. pic.twitter.com/PjeCyKwZRU
Survival Analysis to Explore Customer Churn in Python https://t.co/A3Tb9RCHsd pic.twitter.com/IdUnLgqJu5
— KDnuggets (@kdnuggets) June 27, 2018
Liza Darrous did a great presentation on circular visualisations @RLadiesLausanne recently! I used jokergoo’s circlize package for a quick #tidytuesday spin. circlize can use tibble data directly but some formatting options are only for matrix. https://t.co/GBrouNj0je #rstats pic.twitter.com/iTTVAsLx1Q
— Xavier (@xvrdm) June 27, 2018
Are you interested in text as data for computational social science? You might be interested in my slides on using information-theoretic methods for text analysishttps://t.co/kMDSLMP6G1 pic.twitter.com/zGQTGqKTVT
— Ryan J. Gallagher (@ryanjgallag) June 26, 2018
I made a dendrogram from chapters 2 + 3 of @tamaramunzner's 'Visualization Analysis and Design,' showing the what/why/how of #dataviz. The book goes deep, but I find this tree outline really helpful when thinking through + making new viz. https://t.co/b5uuJaQamd pic.twitter.com/S2y7i1dM71
— 𝙹𝙸𝙻𝙻 𝙷𝚄𝙱𝙻𝙴𝚈 (@Jill_hubley) June 26, 2018
This is good content (thanks!), but - if you're learning @TensorFlow today - I recommend skipping the graph level stuff - and beginning with tf.keras and eager - unless you have a specific reason to use this older style.
— Josh Gordon (@random_forests) June 26, 2018
* https://t.co/mf4eZxngxi
* https://t.co/XkiVgWczBv https://t.co/gACHuRrFNn
Three patterns for fast prototyping and research in #TensorFlow! https://t.co/78onjX5utv
— Danijar Hafner (@danijarh) June 26, 2018
This NMT with attention colab notebook is one of the cleanest and best documented TF examples I’ve seen: https://t.co/eUzJ4aSthD
— Denny Britz (@dennybritz) June 26, 2018
🤖 feat. @h2oai, caret + more…
— Mara Averick (@dataandme) June 26, 2018
"Using Machine Learning w/ LIME To Understand Employee Churn" ✏️ @bradleyboehmkehttps://t.co/cQnYato5mG via @bizScienc #rstats pic.twitter.com/ggPQuGXvbX
tf.keras programmer's guide is out! On the v1.9 docs pre-release. Highly recommended, this is a great way to develop with @TensorFlow.
— Josh Gordon (@random_forests) June 25, 2018
To try it out you'll need to install the rc: pip install --pre -U tensorflow==1.9.*https://t.co/mf4eZxngxi