Drench yourself in Deep Learning, Reinforcement Learning, Machine Learning, Computer Vision, and NLP by learning from these exciting lectures!!

Ui.Vision Open-Source RPA Software - Robotic Process Automation with Computer Vision and OCR, Selenium IDE compatible.

Programming Assignments and Lectures for Stanford's CS 231: Convolutional Neural Networks for Visual Recognition

Custom Object Detection with TensorFlow

My assignment solutions for CS231n - Convolutional Neural Networks for Visual Recognition

mPLUG-Owl: The Powerful Multi-modal Large Language Model Family

Complete Assignments for CS231n: Convolutional Neural Networks for Visual Recognition

Classify images offline using Watson Visual Recognition and Core ML

[ICLR 2024] Official PyTorch implementation of FasterViT: Fast Vision Transformers with Hierarchical Attention

WARNING: This repository is no longer maintained ⚠️ This repository will not be updated. The repository will be kept available in read-only mode. A mobile app that uses Watson Visual Recognition to provide nutritional analysis of captured food images.

Aerial scout for first responders. DroneAid uses machine learning to detect calls for help on the ground placed by those in need.

Shortest solutions for CS231n 2021-2024

Assignment solutions for the CS231n course taught by Stanford on visual recognition. Spring 2017 solutions are for both deep learning frameworks: TensorFlow and PyTorch.

[ICML 2023] Official PyTorch implementation of Global Context Vision Transformers

work in progress

My assignment solutions for Stanford’s CS231n (CNNs for Visual Recognition) and Michigan’s EECS 498-007/598-005 (Deep Learning for Computer Vision), version 2020.

Official PyTorch Implementation of MambaVision: A Hybrid Mamba-Transformer Vision Backbone

Improved Residual Networks (https://arxiv.org/pdf/2004.04989.pdf)

Create an iOS phone application that sorts waste into three categories (landfill, recycling, compost) using a Watson Visual Recognition custom classifier

Official PyTorch implementation of Fully Attentional Networks