— In the News —
It won't be long before investors stop looking for AI powered startups in the same way they don't look for startups that use databases. AI will just be assumed.
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— Tools and Techniques —
Great overview of the current data engineering ecosystem. The article discusses industry trends and the accompanying interactive offers an easy way to explore the options. This is definitely worth bookmarking.
It's easy to forget that modeling has been around for a long time. This article explores how real-world models from the past relate to machine learning models today.
Nice tutorial for extracting data from a web page. Starts with the basics of understanding the structure of a page and uses movie recommendation sites to show how to parse, extract, and analyze a site's data.
This is a high-level, executive perspective of things to think about when designing your infrastructure. Along with being useful for decision-makers, this big-picture view will help data scientists and engineers understand the thinking behind the systems they're working with.
— Resources —
Great collection of curated Jupyter notebooks covering a wide variety of topics including things like statistics, programming, signal processing, data visualization, math, natural language processing, scientific computing, etc.
This curated collection of medical-related datasets offers a lot of possibilities for building machine learning applications. There are a wide variety of datasets here, including imaging data (such as brain scans and retinal images), data derived from Electronic Health Records, data about diseases, etc.
— Deep Learning —
Here's the official version the of the hotdog / not-hotdog app that was recently featured on an episode of Silicon Valley. Yes, the HBO Show actually built this app. This is a fantastic tutorial that includes the thinking behind the app, architecture, training details, insights about running neural nets on mobile phones, UX decisions, lessons learned, etc.
— Data Viz —
This is a dense and insightful slide deck by Tamara Munzner. Tamara is well-known in the visualization community. She's published over sixty-five papers and her book, Visualization Analysis and Design, appeared in 2014.
— About —
Data Elixir is curated and maintained by @lonriesberg. If some awesome person forwarded this issue to you, subscribe for free at dataelixir.com and get it delivered every week.