HN user

cdl

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medium.com 7y ago

Data Science Is More Than Machine Learning

cdl
1pts0
github.com 7y ago

Show HN: An Enterprise-Grade Data Science Case Study (With Docs and Code)

cdl
2pts1
xkcd.com 7y ago

Modified Bayes' Theorem

cdl
1pts0
mbmlbook.com 7y ago

Model-Based Machine Learning

cdl
12pts2
arxiv.org 7y ago

The LKPY Package for Recommender Systems Experiments

cdl
3pts0
arxiv.org 7y ago

A Survey of Learning Causality with Data: Problems and Methods

cdl
1pts0
rasa.com 7y ago

Open source conversational AI

cdl
1pts0
71republic.com 8y ago

A Gentle Conceptual Introduction to Supply-Side and Demand-Side Economics

cdl
3pts0
agi.mit.edu 8y ago

MIT 6.S099: Artificial General Intelligence

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174pts3
www.washingtonpost.com 8y ago

U.S. economic growth slowed in 2017’s fourth quarter

cdl
1pts0
www.marketwatch.com 8y ago

Kodak climbs on the blockchain bandwagon

cdl
1pts0
github.com 8y ago

Production Data Science

cdl
2pts0
brohrer.github.io 8y ago

Imposter Syndrome (in Data Science)

cdl
45pts12
www.theglobeandmail.com 9y ago

Coding is the new cursive writing – and we have to embrace it

cdl
12pts3
weather.gc.ca 10y ago

Environment Canada's Temperature and Precipitation Probabilistic Forecasts

cdl
1pts0
www.cs.berkeley.edu 11y ago

Global seismic monitoring: A Bayesian approach [pdf]

cdl
1pts0
blog.echonest.com 11y ago

Plotting Music’s Emotional Valence (2013)

cdl
1pts0
density-fns.herokuapp.com 11y ago

Show HN: I built a a web app to visualize density functions

cdl
2pts0
eml.berkeley.edu 11y ago

Distinguishing Informational Cascades from Herd Behavior (2003) [pdf]

cdl
2pts0
mlg.eng.cam.ac.uk 11y ago

Probabilistic Modelling, Machine Learning, and the Information Revolution (2012) [pdf]

cdl
3pts0
research.microsoft.com 11y ago

Building statistical models by visualization

cdl
2pts0
blog.codecentric.de 11y ago

Elasticsearch Indexing Performance Cheatsheet

cdl
3pts0
en.wikipedia.org 11y ago

Table of conjugate distributions

cdl
1pts0
en.wikipedia.org 11y ago

Common Probability Distributions

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2pts0
mobiledevmemo.com 12y ago

Avoiding Simpson's paradox in data analysis

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4pts0
gigaom.com 12y ago

Judge’s ruling spells bad news for U.S. cloud providers

cdl
3pts0
www.perceptualedge.com 12y ago

Practical Rules for Using Color in Charts

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1pts0
www.reactive-streams.org 12y ago

Reactive Streams

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2pts0
blogs.hbr.org 12y ago

The Irresistible Power of Storytelling as a Strategic Business Tool

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2pts0
www.eecs.berkeley.edu 12y ago

An Architecture For Fast and General Data Processing On Large Clusters

cdl
2pts0

IMO it's a great educational tool to understand how to build AI apps at least at a high level. It provides a good mental model. Can someone name another single resource that achieves the same?

As aspiring data scientists or data scientists new to the field (and working in a business-centric rather than a research-centric role), it is easy to be blinded by the technical aspects of data science such as exploratory data analysis and machine learning, failing to see the forest for the trees.

My goal with this post and accompanying GitHub project is to provide a more accurate example of an enterprise-grade data science project that includes what is often missed in these sorts of examples: the rigorous positioning of a business problem and scoping of a data science solution.

The majority of the time spent to complete this project was allocated to the understanding and definition of the business problem rather than optimizing a machine learning model. As with any other complex problem, spending some time upfront to work through the various aspects of a business problem in a systematic way will reduce the risk of coming up with the wrong solution, which clearly wastes time (read: opportunity loss) and money.

I think "making AI accessible to every business" is a bit of stretch. While there's no doubt that the AutoML suite will bring tremendous benefits to businesses with recommendation and speech and image recognition needs, it falls short of providing more useful insights such as those gleaned by association rules, clustering (i.e. segmentation), and general probabilistic models.

I think that if AI is to be accessible to every business then it will deliver insights rather than the machinery to produce the insights. This is especially true in the context of small businesses.

They are counter-intuitive and make code unnecessarily harder to read and reason about. It doesn't make sense to first be presented with a predicate and then have to figure out the subject.

Check out What's New for Visual F# in Visual Studio 2012

http://msdn.microsoft.com/en-us/library/hh370982.aspx

New features include:

Type Providers - OData (Open Data) Services - Database Connections such as SQL - Database Schema - Data specified by the Entity Data Model format - Web services in the WSDL format.

Query Expressions

Auto-implemented properties

Parameter Help

Enhanced IntelliSense

Create applications that run against the following versions of the .NET Framework: 2.0, 3.0, 3.5, 4, and 4.5