Neural networks: Difference between revisions

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* [https://dl.acm.org/doi/10.1145/3315454.3329960 Convolutional neural networks in APL] is a paper that shows how a Convolutional Neural Network (CNN) can be implemented in APL. The code is available in [https://github.com/ashinkarov/cnn-in-apl a GitHub repository]. A video of the presentation of this conference paper is available [https://www.youtube.com/watch?v=9vIZ7d3-GBw here].
* [https://dl.acm.org/doi/10.1145/3315454.3329960 Convolutional neural networks in APL] is a paper that shows how a Convolutional Neural Network (CNN) can be implemented in APL. The code is available in [https://github.com/ashinkarov/cnn-in-apl a GitHub repository]. A video of the presentation of this conference paper is available [https://www.youtube.com/watch?v=9vIZ7d3-GBw here].


* Mike Powell's [[Media:3_Machine_Learning.pdf|Machine Learning]] is a tutorial that explores pattern recognition for handwritten digits, focusing  It focuses on deriving the analytic gradients and establishing them as part of the back propagation algorithm.
* Mike Powell's [[Media:3_Machine_Learning.pdf|Machine Learning]] is a tutorial that explores pattern recognition for handwritten digits. It focuses on deriving the analytic gradients and establishing them as part of the back propagation algorithm.


* Mike Powell's [[Media: 4_The_Handwritten_Digits_Model.pdf|The Handwritten Digits Model]] tutorial explores how to estimate the parameters of the handwritten digits model using an interactive session, with the objective to explore how to conduct an estimation rather than to find the absolute best search algorithm.
* Mike Powell's [[Media: 4_The_Handwritten_Digits_Model.pdf|The Handwritten Digits Model]] tutorial explores how to estimate the parameters of the handwritten digits model using an interactive session, with the objective to explore how to conduct an estimation rather than to find the absolute best search algorithm.

Revision as of 14:48, 8 December 2020

Neural networks (NNs), or more precisely artificial neural networks (ANNs), are computing systems vaguely inspired by the biological neural networks of brains.

This is a list of APL resources relating to neural networks:

  • Mike Powell's Machine Learning is a tutorial that explores pattern recognition for handwritten digits. It focuses on deriving the analytic gradients and establishing them as part of the back propagation algorithm.
  • Mike Powell's The Handwritten Digits Model tutorial explores how to estimate the parameters of the handwritten digits model using an interactive session, with the objective to explore how to conduct an estimation rather than to find the absolute best search algorithm.
  • ANNSER ― A Neural Network Simulator for Education and Research
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