Envisioning Neural Nets As Org Charts

Maiia Bakhova describes the layout of a neural net as similar to a chain of command within an organization:

We can observe a lot of in common with a corporation chain of command. As we see middle managers are hidden layers which do the balk of the job.  We have the similar information flow and processing which is analogous to forward propagation and backward propagation.
What is left now is to explain that  dealing with sigmoid function at each node is too costly so it mostly reserved for CEO level.

That’s a metaphor I hadn’t heard before.

Related Posts

Where Machine Learning And Econometrics Collide

Dave Giles shares some thoughts on how machine learning and econometrics relate: What is Machine Learning (ML), and how does it differ from Statistics (and hence, implicitly, from Econometrics)? Those are big questions, but I think that they’re ones that econometricians should be thinking about. And if I were starting out in Econometrics today, I’d […]

Read More

Auto ML With SQL Server 2019 Big Data Clusters

Marco Inchiosa has a model scenario for using Big Data Clusters to scale out a machine learning problem: H2O provides popular open source software for data science and machine learning on big data, including Apache SparkTM integration. It provides two open source python AutoML classes: h2o.automl.H2OAutoML and pysparkling.ml.H2OAutoML. Both APIs use the same underlying algorithm implementations, […]

Read More

Categories

June 2017
MTWTFSS
« May Jul »
 1234
567891011
12131415161718
19202122232425
2627282930