Angular penalty loss functions in Pytorch (ArcFace, SphereFace, Additive Margin, CosFace)

Fashion MNIST as a Service that is using Mlflow and Tensorboard for reporting on Docker.

Project Deep Learning about Computer Vision task with FMNIST dataset

Generating Fashion MNIST data using Auxiliary Classifier GAN.

Applying Dimensionality Reduction algorithms i.e PCA, LDA, FDA on CIFAR-10, MNIST, F-MNIST dataset

Here are Jupyter Notebooks that demonstrate pruning and quantization of the Fashion-MNIST Classification model in Pytorch. Also, using Torchscript, one can easily deploy these models on a C++ platform.

Merged Geometrical Homogeneous Clustering for Image Data Reduction. An algorithm to reduce large image datasets maintaining similar accuracy.

This is a concise tutorial on applying PCA in the benchmark dataset Fashion MNIST. I analyse how the data compression process is done in visual information.

This notebook demonstrate the use of PyTorch to create a Multi-Layer Perceptron for Image Classification on Fashion Mnist Dataset.

Compilation of different ML algorithms implemented from scratch (and optimized extensively) for the courses COL774: Machine Learning (Spring 2020) & COL772: Natural Language Processing (Fall 2020)

This repository created for studying GAN Models

A Python3 repository for NLP and machine learning enthusiasts. Explore Twitter tweet classification using federated learning techniques. Leverage the power of MNIST and FMNIST datasets for training and evaluation of models. Dive into the world of federated machine learning with Twitter data!

All assignments of Statistical Machine Learning Course

Building neural networks using TensorFlow to recognize different clothes from Fashion-MNIST dataset

fMNIST data set classification with GAN, Semi-supervised learning with GAN

Ensemble is a machine learning concept in which multiple models are trained using the same learning algorithm

Predicting the clothing details in an unlabeled image can facilitate the discovery of the most similar fashion items in an e-commerce database.

The project contains the implementation of a Feed Forward Neural Network using Keras on the FMNIST Dataset.

Training latent spaces in a DCNN Autoencoder network with the FMNIST dataset.

Implementation of the VGG-16 Algorithm on CIFAR-10