himanshukandwal / tensorflow-mnist-tutorial

Sample code for "Tensorflow and deep learning, without a PhD" presentation and code lab.

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This is support code for the codelab "Tensorflow and deep learning - without a PhD"

The presentation explaining the underlying concepts is here and you will find codelab instructions to follow on its last slide. Do not forget to open the speaker notes in the presentation, a lot of the explanations are there.

The lab takes 2.5 hours and takes you through the design and optimisation of a neural network for recognising handwritten digits, from the simplest possible solution all the way to a recognition accuracy above 99%. It covers dense and convolutional networks, as well as techniques such as learning rate decay and dropout.

Installation instructions here. The short version is: install Python3, then pip3 install tensorflow and matplotlib.

The most advanced advanced neural network in this repo achieves 99.5% accuracy on the MNIST dataset (world best is 99.7%) and uses batch normalization.


Disclaimer: This is not an official Google product but sample code provided for an educational purpose

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Sample code for "Tensorflow and deep learning, without a PhD" presentation and code lab.

License:Apache License 2.0


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