A neural network mimics brain processing using layers of interconnected neurons. It includes an input layer (features), hidden layers (processing units), and an output layer (results). Activation functions (e.g., ReLU, sigmoid) introduce non-linear feedforward neural network in Keras for binary classification with layers: input, hidden, and output.

This repository contains an implementation of a Deep Convolutional Generative Adversarial Network (DCGAN) trained on the FashionMNIST dataset. The project aims to generate realistic images of clothing items using a GAN architecture. It includes model definitions, training scripts, and visualizations of generated images at various training stages.

The project aims to use modern data science tools

Application of Wasserstein Generative Adversarial Networks on Fashion MNIST using PyTorch

SLIIT 4th Year 2nd Semester Machine Learning Project

Une série de notebooks qui expliquent en détail comment fonctionnent les modèles de diffusion

This repo contains the PyTorch implementation of the paper 'Adversarial Training for Free!', which can be found here: https://arxiv.org/pdf/1904.12843.pdf

Generative Adversarial Networks in TensorFlow 2.0

Fashion-Mnist classification

A pipeline built on MetaFlow for training Fashion MNIST dataset using Pytorch, experiment tracking using MLFlow and model deployment using BentoML

Pytorch implementation of a denoising autoencoder.

classification of fashion data(28 x28 greyscale image) into 10 classes.

FashionMNIST - Logistic regression

A consortium of popular ML algorithms/concepts implemented in Python.

One-Shot Learning with Triplet CNNs in Pytorch

Fashion Mnist image classification using cross entropy and Triplet loss

image classification and manipulation in python machine learning on fashion mnist dataset

ML project for Content Based Image Recognition using Keras