Training latent spaces in a DCNN Autoencoder network with the FMNIST dataset.
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Updated
May 12, 2024 - Python
Training latent spaces in a DCNN Autoencoder network with the FMNIST dataset.
The project contains the implementation of a Feed Forward Neural Network using Keras on the FMNIST Dataset.
Angular penalty loss functions in Pytorch (ArcFace, SphereFace, Additive Margin, CosFace)
Project Deep Learning about Computer Vision task with FMNIST dataset
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!
Building neural networks using TensorFlow to recognize different clothes from Fashion-MNIST dataset
Fashion MNIST as a Service that is using Mlflow and Tensorboard for reporting on Docker.
Implementation of the VGG-16 Algorithm on CIFAR-10
Applying Dimensionality Reduction algorithms i.e PCA, LDA, FDA on CIFAR-10, MNIST, F-MNIST dataset
Ensemble is a machine learning concept in which multiple models are trained using the same learning algorithm
Training FMNIST and MNIST dataset on Feed Forward Neural Network
Merged Geometrical Homogeneous Clustering for Image Data Reduction. An algorithm to reduce large image datasets maintaining similar accuracy.
Clean and simple implementation of a recognizer for FMNIST data.
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 repository created for studying GAN Models
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)
Fashion MNIST dataset processing using logistic regression, neural network and convolutional neural networks. Dataset source: https://www.kaggle.com/zalando-research/fashionmnist
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.
Predicting the clothing details in an unlabeled image can facilitate the discovery of the most similar fashion items in an e-commerce database.
Generating Fashion MNIST data using Auxiliary Classifier GAN.
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