Learn how to design, develop, deploy and iterate on production-grade ML applications.

Learn OpenCV : C++ and Python Examples

The repository provides code for running inference with the Meta Segment Anything Model 2 (SAM 2), links for downloading the trained model checkpoints, and example notebooks that show how to use th…

A collection of pre-trained, state-of-the-art models in the ONNX format

A course in reinforcement learning in the wild

PyTorch implementation of AnimeGANv2

Contains Solutions and Notes for the Machine Learning Specialization By Stanford University and Deeplearning.ai - Coursera (2022) by Prof. Andrew NG

Official codebase used to develop Vision Transformer, SigLIP, MLP-Mixer, LiT and more.

Hands-On Reinforcement Learning with Python, published by Packt

Some fun and useful projects with Python

Facenet implementation by Keras2

This is a sample program that recognizes hand signs and finger gestures with a simple MLP using the detected key points. Handpose is estimated using MediaPipe.

Programming assignments and quizzes from all courses within the GANs specialization offered by deeplearning.ai

Assignments and notes for the Self Driving Cars course offered by University of Toronto on Coursera

This repo contains the updated version of all the assignments/labs (done by me) of Deep Learning Specialization on Coursera by Andrew Ng. It includes building various deep learning models from scra…

YOLOv7 Object Tracking using PyTorch, OpenCV and DeepSORT

Self -driving cars specialization by the university of toronto at coursera

I have published my face related codes in this repository

Pybullet codes for robotics

Coursera Generative Adversarial Networks (GANs) Specialization

One Shot Learning Implementation

Using a U-Net model to segment clothing items in an image

This is the implementation of the unit LSTM cell

Using UNet to segment and clasisify the tumours on an MRI brain scan

Exploring non-gradient-based learning techniques for training neural networks, using brute force parameter search and optimization methods. Includes comparison with gradient-based learning.

A deep learning project by the RAIN-INN community focused on accurately classifying various types of fruits, transfer learning, data augmentation, and image processing.

This Is an updated and more improved version of the Toxic Comment Classification with an LSTM I did earlier