Issue-number: 204

summary: Jupyter tips & tricks. Sports analtyics/viz. Mathematics as thought. A Principled Bayesian Workflow. ML optimization tutorial. PDF table extraction. number: 204 title: Issue 204 url: published_at16.10.18, 16:35

Issue-number: 195

summary: Wrong about bias? Black Hat data. Sign language for Alexa. Julia 1.0. What’s New w/ TensorFlow? CS books for data scientists. Data viz trickery. number: 195 title: Issue 195 url: published_at14.08.18, 18:15

Issue-number: 196

summary: Notebook innovation. Stats & sports. ML cheatsheets. Exploring correlations. R Music. Civic hacking. Cartography Playground. number: 196 title: Issue 196 url: published_at21.08.18, 16:04

Issue-number: 197

summary: Notebooks: the bad parts. JupyterCon highlights. ML startups to watch. NLP generalization. Text mining tutorial. Real-world data compression. number: 197 title: Issue 197 url: published_at28.08.18, 16:53

Issue-number: 198

summary: Python tricks. Models Will Run the World. How to deploy a model as an API. R package dev. Data-driven programming. Storage innovation. number: 198 title: Issue 198 url: published_at04.09.18, 16:26

Issue-number: 199

summary: Notebook wars. Command line ethics. Why data culture matters. Scaling knowledge w/ graphs. Searching for data. Movie recommendation tutorial. number: 199 title: Issue 199 url: published_at11.09.18, 18:54

Issue-number: 200

summary: Career roadmap. What-If Tool. Data center innovation. Anatomy of an AI. Phases of data analysis. Code as configuration. Boxplots. number: 200 title: Issue 200 url: published_at18.09.18, 19:38

Issue-number: 201

summary: xkcd: Curve Fitting. Fixing ML reproducibility. Matplotlib tips/demos. AI: generating $$ vs saving $$. number: 201 title: Issue 201 url: published_at25.09.18, 16:41

Issue-number: 202

summary: Own your data. Leveling-up your ML skills. Why Julia? Custom loss functions. Experimenting w/ SEO. Decision-trees. ML for visualization. number: 202 title: Issue 202 url: published_at02.10.18, 16:40

Issue-number: 203

summary: Blind spots of the data-driven. Hacker’s guide to uncertainty. How to deliver on ML projects. State of NLP. A/V from R. Evolving Data PM roles. number: 203 title: Issue 203 url: published_at09.10.18, 18:56