A flexible utility for converting tensor precision in PyTorch models and safetensors files, enabling efficient deployment across various platforms.

A comprehensive toolkit built with PyTorch, designed to facilitate the training, evaluation, and visualization of autoencoders. From simple linear autoencoders to convolutional and variational architectures, this project offers an intuitive and expandable framework for anyone delving into the realm of unsupervised learning.

Features injected recurrent neural networks for short-term traffic speed prediction

Computer Vision State Of The Art Intuition Project in Pytorch; WIP

자연어처리

Developed a CNN model to classify skin moles as benign or malignant using a balanced dataset from Kaggle, achieving a test accuracy of 81.82% and an AUC of 89.06%. Implemented data preprocessing by resizing images to 224x224 pixels and normalizing pixel values, enhancing model performance and stability.