ROS node for real-time FCNN depth reconstruction

Concise neural network with C++ and CUDA

Predicting the closing stock price given last N days' data that also includes the output feature for CNN & LSTM, while predicting it for regular NN given only today's data, observing and comparing time series for various models. Additionally finding best value for N previous days and bidirectional LSTM for experiments.

Example of converting CNN network to FCNN using keras.

A collection of Deep Learning labs for the INSAT Data Science course.

Enjoy the major Deep Learning Projects !!!

This project is about training a deep neural network to identify and track a target in simulation using Udacity's RoboND drone simulator. 🛸 Applications like this are key to many fields of robotics and the techniques applied can be extended to scenarios like advanced cruise control in autonomous vehicles or human-robot collaboration. 👨‍🏫

Neural networks on digit recognition. As part of the MITx course on machine learning with Python - from linear models to deep learning

A ROS1 self-driving car for the "Autonomous Driving Competition". The vehicle itself is a DonkeyCar with RaspberryPi and Raspberry Pi Camera.

Fully Convolutional Neural Network for heart segmentation

This a simple C++ fully connected neural network class for Course COMP3046.

The project utilizes convolutional-based network architectures for semantic image segmentation. This project utilizes the Indian Driving Dataset (IDD) Lite to classify various elements in Indian road scenes into 8 predefined categories. This implementation utilizes FCN - VGG backbone, U-Net, LINKNet, and DEEPLABV3+.

This project demonstrates a complete pipeline for recognizing handwritten digits using the MNIST dataset. The project is implemented in Python using Jupyter Notebook, and it covers data loading, preprocessing, model training, and performance evaluation of a Fully Connected Neural Network (FCNN).

Detection of Diabetic Retinopathy using a Fully Connected Neural Network for Data Processing (Tensorflow 1.12.0 and Python 3.6.6)

Classification of 3 species of flowers (versicolor, virginica, setosa) belonging to the Iris family, using a Fully Connected Neural Network for Data Processing (Tensorflow 1.12.0 and Python 3.6.6)

An implementation of the iris flower classification using Keras on the iris dataset

Implementations of components of Neural Networks from scratch in Numpy

Implementaion of Random Forest, CNN and FCNN on 'Student Performance Factors' dataset

Train FCNN to label each pixel of driving footage as being part of the road (or not). For use in perception systems of autonomous cars.

Building fully connected neural network from zero without using deep learning libraries such as Pytorch.