Tag: Neural Networks

The Buddhabrot
Deep LearningHard Sciences StudyMachine Learning
June 21, 2020 1 Comment

Chaos

Nietzsche, in the premise of Thus Spoke Zarathustra, stated Eternal Return, an idea that all things in the universe will recur and all events in one’s life will repeat again, and again, ad infinitum1. Nietzsche’s world was a deterministic one, Newtonian, with tangible objects, and countable sets. Hence, he viewed his universe like a deck of cards, bound to return to a particular shuffle infinitely on infinite shuffles. But all recurrences need not be perfectly self similar. Far older eyes have imagined universes in kalpas, forever repeating...

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Computer VisionDeep LearningGithub Projects
January 5, 2020 Post a Comment

Conv Nets IV

In this project, I extend my CNN Advanced Architecture project to 3D images. The architecture is advanced in the sense that it is flexible and can read & build its architecture from JSON files. The 3D extension allows CNN3D classifier to tackle 3D tensors used heavily in scientific/medical imaging such as CT Scan images, which we will we working with in this repo. I have trained the LUNA16 dataset with the usual my CNN and then with CNN3D classes and noted that CNN3D outperfromed the predictions for...

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Computer VisionDeep LearningGithub Projects
November 27, 2018 Post a Comment

Conv Nets II

In this project we automate learning CNN hyper-params. We test and improve my previous tensorflow implementation of CNN in here on Kaggle's facial expression dataset. Here I only deal with two label classes: Happy and Angry. Randomized Search for learning hyper_paramters such as learning_rate, momentum coefficient, etc and drop-out regularizer improved the default test prediction accuracy from 36% to 64%. This is a bit lower than 72% that we got from our SVM classifier. However, we will continue in following projects to see if we can beat...

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Computer VisionDeep LearningGithub Projects
September 14, 2018 Post a Comment

Conv Nets I

This project implements CNN class using the tensorflow library. I compare the accuracies gained by the ConvPool layers to the ANN architectures of the ANN-tensorflow repo. We find that adding only 2 ConvPool layers before the ANN architecture does automated feature engineering and significantly increases the classification accuracies as compared to the ANN only architectures....