Implementation of the Random Dilated Shapelet Transform algorithm along with interpretability tools. ReadTheDocs documentation is not up to date with the current version for now.
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Updated
Jan 11, 2024 - Python
Implementation of the Random Dilated Shapelet Transform algorithm along with interpretability tools. ReadTheDocs documentation is not up to date with the current version for now.
Pruning System in Keras for a Deeper Look Into Convolutions
This program displays an animation of two functions being convolved together with custom user-defined functions supported.
A Tensorflow CNN based model for playing battleship as efficiently as possible.
Some models built from scratch with PyTorch during my graduate program at UT Austin
Some models built from scratch with PyTorch during my graduate program at UT Austin
This is a simple deep learning model to detect whether a person is happy or sad.
Python Library for creating and training CNNs. Implemented from scratch.
Exploring image colour space transformations and augmentation for creating a classifier to characterise parasitized and uninfected RBCs. Proposes a CNN model that uses the Saturation of the HSV colour model to create a high quality classifier resulting in accuracies of 99.3% and above.
📷 Web application to visualize several different convolutions by using image kernels to apply effects such as sharpening, edge detection, blurring, and more!
Computer Vision State Of The Art Intuition Project in Pytorch; WIP
Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning
Transfer Learning, Convolutions, and Object Localisation in Keras
Signal Analysis projects and a final project involving the generation of echo in sound waves using Matlab
Covering all aspects of Laplace Transforms that could be covered in a first semester Differential Equations curriculum.
Create a convolutional layer from scratch in python, hack its weights with custom kernels, and verify that its results match what pytorch produces.
A Pytorch implementation of the paper "Going deeper with convolutions" by Szegedy et. al. 2014
A lightweight deep learning framework
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