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Created page with "Mike Powell is doing research into Machine Learning using APL. == Papers == * The Derivative Revisited (2020) A fresh look at Ken..."

Mike Powell is doing research into Machine Learning using APL.

== Papers ==

* [[Media:1_The_Derivative_Revisited.pdf | The Derivative Revisited (2020)]]

A fresh look at Ken Iverson's 1979 paper "The Derivative Operator".

* [[Media:2_The_Derivative_Rules.pdf | The Derivative Rules (2020)]]

In 1979 Ken Iverson wrote a paper on the derivative as part of the ACM APL Conference. That paper included a section summarizing the rules for the derivatives of compound formulations such as fĂg. His summary omits the derivation of these rules. This article aims to restate these rules in more modern notation and provide derivations for Iverson's results.

* [[Media:3_Machine_Learning.pdf|Machine Learning (2020)]]

This tutorial explores the pattern recognition model for handwritten digits using data from the Modified National Institute of Standards and Technology database. It focuses on deriving the analytic gradients in APL and establishing them as part of the back propagation algorithm.

* [[Media: 4_The_Handwritten_Digits_Model.pdf|The Handwritten Digits Model (2020)]]

This tutorial explores how to estimate the parameters of the handwritten digits model. It does so using an interactive session. The objective is to explore how to conduct an estimation rather than to find the absolute best search algorithm.

== Papers ==

* [[Media:1_The_Derivative_Revisited.pdf | The Derivative Revisited (2020)]]

A fresh look at Ken Iverson's 1979 paper "The Derivative Operator".

* [[Media:2_The_Derivative_Rules.pdf | The Derivative Rules (2020)]]

In 1979 Ken Iverson wrote a paper on the derivative as part of the ACM APL Conference. That paper included a section summarizing the rules for the derivatives of compound formulations such as fĂg. His summary omits the derivation of these rules. This article aims to restate these rules in more modern notation and provide derivations for Iverson's results.

* [[Media:3_Machine_Learning.pdf|Machine Learning (2020)]]

This tutorial explores the pattern recognition model for handwritten digits using data from the Modified National Institute of Standards and Technology database. It focuses on deriving the analytic gradients in APL and establishing them as part of the back propagation algorithm.

* [[Media: 4_The_Handwritten_Digits_Model.pdf|The Handwritten Digits Model (2020)]]

This tutorial explores how to estimate the parameters of the handwritten digits model. It does so using an interactive session. The objective is to explore how to conduct an estimation rather than to find the absolute best search algorithm.

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