This repository contains worked examples that I found useful when following the Max Planck Institute course 'Machine Learning for Physicists' https://machine-learning-for-physicists.org/
The ExampleNotebooks folder contains several files (.py converted from .ipynb files) that illustrate several examples of convolutional neural networks.
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The CNNTraining file contains a simple example for training a convolutional network to recognise shapes (circles and squares)
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The DenoiseCNN file contains a denoising auto-encoder, using the same shape recognition network from CNNTraining
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The DigitRecognition contains an example network that recognises digits using the MNIST handwritten digits database.
The WrittenExamples folder contains python files that I wrote to practice implementing simple neural networks from scratch (no libraries).
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The IntroNetwork file contains a manual implementation of a simple sequential network with three hidden layers.
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The BackpropNetwork file implements backpropagation to the IntroNetwork.