Deep Learning implementations using PyTorch
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Updated
Feb 26, 2022 - Python
Deep Learning implementations using PyTorch
This repository contains all the work done by me for Coursera's Deep Learning Specialization.
Residual Network for classifying the CIFAR-10 dataset
Convolutional Neural Networks coding assignments
Implementation of ResNet, and a myraid of Normalization layers, in PyTorch
Residual neural network in Rust for modeling binary numbers
ResNet, residual network, implementation in Keras, for image classification, with different model architecture depths
Deep Learning Specialization - Coursera
Attempt on Residual Neural Network based on ResNet 50. Applied on SIGNS dataset
Tests I am performing on a Python package for building residual multi - layer perceptrons and tandem [any model] -> ResMLPs models, useful for effective transfer learning. A pypi package should be coming soon.
High Accuracy ResNet Model under 5 Million parameters.
A simple app that predicts which Simpson character you make it see! Here is an example of it in action:
Text Summarization using Residual Logarithmic LSTMs
Repository experimenting with predicting binding affinities inspired by DeepLigand
ResNet model for detecting abnormalities in x-ray imaging
Download the dataset from here: https://www.kaggle.com/alexattia/the-simpsons-characters-dataset/data
CNN, ResNets and Computer Vision
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