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10 Statistical Techniques You Need to Know. Full Stack Data Science. Migration. [DSR #111]
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There is a growing sense that neural networks need to be interpretable to humans. The ﬁeld of neural network interpretability has formed in response to these concerns. As it matures, two major threads of research have begun to coalesce: feature visualization and attribution. This article focusses on feature visualization.
This is a fascinating read with some great images. It illustrates just how early we are at understanding the behaviors of neural networks and the cutting edge research that’s going on to push us forwards.
While having a strong coding ability is important, data science isn’t all about software engineering. I personally know too many software engineers looking to transition into data scientist and blindly utilizing machine learning frameworks such as TensorFlow or Apache Spark to their data without a thorough understanding of statistical theories behind them.
Couldn’t agree more. This post is both an excellent index of techniques as well as a very readable introduction to each of them.
Solid list. I need to read Andrew Ng’s book.
Data visualization practitioners are a motley group, and while no two may look exactly alike, they all fall into one of 7 distinct categories.
This is very amusing. You will identify with at least one of the categories and will likely have made fun of people who come from several others.
Sorry for three listicles in a row.
In this talk, we walked through an actual Indeed data science full-stack model building process: labeling data, performing analysis, generating features, building the model, validating the model, building infrastructure, deploying the model, and monitoring the solution.
This is both an excellent example of visualization and a critical piece of work for thinking about the future of data science and AI. The places where researchers migrate will become the centers of gravity for the industry.
So far all text generation models based on neural networks and deep learning have had the same, surprisingly human, limitation: like us, they can only produce language word by word or even letter by letter. Today Salesforce is announcing a neural machine translation system that can overcome this limitation, producing translations an entire sentence at a time in a fully parallel way. This means up to 10x lower user wait time, with similar translation quality to the best available word-by-word models.
An impressive example of rebuilding an entire algorithm bottom-up to be parallelized. This is a major step forwards in a very hot research area.
This article is really old (2009!), but I’m including it as a shout out to an awesome conversation that happened over in dbt’s Slack. A dozen-ish people weighed in on their data modeling practices, whether they strictly adhered to Kimball (most people) or think that Kimball contains useful concepts but needs an update (me).
If you do any work in a data warehouse, you need to at the very least be familiar with Kimball dimensional modeling concepts, and if you’re interested in talking with other people obsessed with this stuff, join us in Slack channel #modeling :)
Google is experimenting with collecting air quality from its street view cars. What other map layers can we hope for? As more of this data becomes API-accessible, use cases abound.
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The internet's most useful data science articles. Curated with ❤️ by Tristan Handy.
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