Machine learing and deep learning code at ancun
-
Updated
Sep 5, 2018 - Jupyter Notebook
Machine learing and deep learning code at ancun
Implementation of different approaches to recommendation on Amazon Review dataset
Study and implementation about deep learning models, architectures, applications and frameworks
Autoencoder coloring gray scale pictures. An artificial neural network constructed and trained with Tensorflow 2.
Credit card fraud classification using handcrafted features, feature extraction algorithms and ensemblin.
Affliated with Leibniz Institute for Neurobiology, Magdeburg and AI Lab, OVGU Magdeburg, we implemented data analysis for single photon calcium imaging with deep learning.
Projects and exercises of the Udacity Deep Learning Nanodegree.
This project demonstrates how an autoencoder can be used as a self-supervised classifier for the MNIST dataset.
An implementation of autoencoding piped with a classification of the mnist dataset
Seeing financial transactions in lower dimensions with neural networks
Search and Compare Cyclones
A pytorch implementation of Variational Autoencoder (VAE) and Conditional Variational Autoencoder (CVAE) on the MNIST dataset
Convolutional autoencoder reducing traffic sign images to 1/6 of their original size.
Autoencoder-based Recommendation System: Integrating Spark and Deep Learning models
Anomaly Detection Basics
Given a video-clip we retrieve k-most similar to it from a "database". We extract features from 3 modalities (audio-video-text) and create an embedding using an AutoEncoder.
Add a description, image, and links to the autoencoder topic page so that developers can more easily learn about it.
To associate your repository with the autoencoder topic, visit your repo's landing page and select "manage topics."