Training code for facial landmark detection based on deep convolutional neural network.
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Updated
Mar 10, 2021 - Python
Training code for facial landmark detection based on deep convolutional neural network.
A novel approach in training a DenseNet model for diagnosing COVID-19 Chest X-Rays.
convolution neural network library in C# with Pretrained models based on convnetjs
1D Convolution Interactive Visualization build with d3.js
A general framework that leverages node feature information (e.g., text attributes) to efficiently generate node embeddings for previously unseen data. Instead of training individual embeddings for each node, GraphSAGE learn a function that generates embeddings by sampling and aggregating features from a node’s local neighborhood. Here, the impl…
Atrial Fibrillation Classification via 1D-CNN and 2D-CNN
Computer Vision: State of the Art model implementation using PyTorch framework.
Worked on the Project Pneumonia Detection using Chest X-Ray in the Phase 1 Period of Bertelsmann Nano Degree Scholarship Program
Estimating power system inertia using convolutional neural network
🚘 Driving a car in simulator using Behavioral Cloning.This project is created for Self Driving Car Nanodegree
U-Net with Python
Repository containing models based on ideas of Machine learning and Deep learning
Building Convolution Neural Networks from Scratch
This repository consists python code for conjunctivitis also known as pink eye image detection using convolution neural network
It is Web App built using Convolution Neural Network (CNN), Fashion MNIST greyscale Image Dataset & python libraries like Tensorflow, Keras, Flask, Numpy, Matplotlib which Predicts Fashion tag for Provided Image.
Applied Deep Learning : Convolution Neural Networks
Implement neural style transfer to change the style of art paintings or photos.
Supervised segmentation of 3-dimensional images using a convolutional neural network.
This project is mainly based on Convolution Neural Network and OpenCV.
CNN model Training to minimize the error between the desired command (data recorded) and the computed steering command output (from the CNN)
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