DataS-DHSC/tech-club

Materials for Statistics and Data Science (SDS) Tech Club sessions

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vonexel/smog

Pytorch implementation of Semantic Motion Generation - motion synthesis from text via CLIP & Kolmogorov-Arnold-Networks

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Omar10lfc/Drug-Discovery-Generation-Project

This project focuses on generative deep learning models for drug discovery, specifically using autoencoder architectures (such as VAE, Conditional VAE, Sparse AE, and Denoising AE) to generate novel molecular structures. It leverages the QM9 molecular dataset, preprocesses molecular data and Evaluating its validity and diversity

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SahilBarbade1203/Druggability_Research_Analysis

RnD project Collaboration

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gd-vae/gd-vae

Geometric Dynamic Variational Autoencoders (GD-VAEs) for learning embedding maps for nonlinear dynamics into general latent spaces. This includes methods for standard latent spaces or manifold latent spaces with specified geometry and topology. The manifold latent spaces can be based on analytic expressions or general point cloud representations.

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jbramburger/DataDrivenDynSyst

Scripts and notebooks to accompany the book Data-Driven Methods for Dynamic Systems

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RichmondDjwerter/Autoencoder-Based-Multi-Modal-Movie-Recommendation-System

This Multi-Modal Movie Recommendation System leverages a combination of structured numerical features and deep text embeddings to provide accurate and personalized movie recommendations. Unlike traditional recommender systems that rely solely on user ratings or metadata, this model integrates numerical attributes (such as popularity and ratings)

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shadoisper/k-sparse-autoencoder

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dikshap07/ML-Algorithms-and-Concepts

Implementation of ML algorithms and concepts from scratch and using scikit learn.

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invcble/Disaster-Risk-Analysis-Platform

Natural Disaster Analysis Website using Deep Learning & Poisson Distribution

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nafiul-araf/BFND-System

This is my academic thesis work (individual). Submitted in partial fulfilment of the requirements for Degree of Bachelor of Science in Computer Science & Engineering

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showpiecep/AutoEncoder

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raghavendranhp/Credit_card_fraud_detection

This repository contains code for a credit card fraud detection model using autoencoders and logistic regression, achieving 95.3% accuracy.

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bhanuchandrika99/NeuralNetwork-DeepLearning

University of Central Missouri: Spring 2024: CS5720: Neural Network Deep Learning: In Class Programming Assignments

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UoM-maul1609/CPI-3V-processing

A collection of scripts and code for processing CPI-3V data

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tgaurav7/Machine-Learning

Application of Machine Learning tools from Python

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Ardawanism/Autoencoder_ML_KNTU_Spring2024

In this repo, a clean and efficient implementation of Fully-Connected or Dense Autoencoder is provided. The code alongside the video content are created for Machine Learning course instructed at Khajeh Nasir Toosi University of Technology (KNTU).

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HayatiYrtgl/autoencoder_deblurring

Python autoencoder to remove blur from images

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umarwaseeem/vae-cifar-10

variational autoencoder trained on cifar-10 dataset for generative image modelling

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amishra15/enhancing-motion-blurred-images-in-normal-and-low-light-condition-for-mobile-photography

DATA: 606 | Capstone Project

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halilergul1/AutoEncoders-HW

autoencoders

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HayatiYrtgl/autoencoder_colorization

Colorizes grayscale images using a loaded model and displays original and predicted colorized versions.

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Aadit3003/Diabetic-Retinopathy_Autoencoder

Autoencoder-based Feature Selection for the SN_DREAMS diabetic retinopathy dataset. (With Prof. S. Raman)

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dariush-bahrami/MyAutoencoders

My implementation of autoencoders

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bhanuchandrika99/NNDL_ICP_Assignment-8

Spring 2024: CS5720: Neural Network Deep Learning: ICP_ Assignment-8

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snajme/DL-Models

This repository is a collection of diverse implementations and variations of deep learning models, including Convolutional Neural Networks, Recurrent Neural Networks, Generative Adversarial Networks, Transformer Models, Variational Autoencoders

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karthik-d/rna-protein-autoencoder

Prediction of surface protein expression from mRNA expression using a regression auto-encoder neural network.

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Shen16/MIE1517_DeepLearning_Lab3

Generative Neural Networks for Horse Image Colourization using Autoencoders. Reconstructing MNIST digits using Convolutional Autoencoders and GANs (Generative Adversarial Networks/ Generator-Discriminator). Adversarial Attack examples with MNIST.

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MediaBilly/Autoencoder-And-Classifier-For-MNIST-Handwritten-Digits

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Chinmayrane16/DeepRecommender

Training Deep AutoEncoders for Collaborative Filtering

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GishB/HTTPRequestClassification

Решение задачи поиска аномальных HTTP запросов (их классификации) к сервису.

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VaishnaviKrishna/bias-field-correction

Bias field correction for T-1 weighted MRI images for tumor detection

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ksasso1028/audio-reverb-removal

Code to train a custom time-domain autoencoder to dereverb audio

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devanshkhare1705/Personalizing-K12-Education

Using deep learning to predict whether students can correctly answer diagnostic questions

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kgkeklikci/ENS492-Graduation-Project-Implementation

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storieswithsiva/CNN-AutoEncoder-DeepLearning

➕💓Let's build the Simplest Possible Autoencoder . ⁉️🏷We'll start Simple, with a Single fully-connected Neural Layer as Encoder and as Decoder. 👨🏻‍💻🌟An Autoencoder is a type of Artificial Neural Network used to Learn Efficient Data Codings in an unsupervised manner🌘🔑

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dangvansam/simple-autoencoder

Audio encoder for reconstruct, denoise image or audio spectrogram

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FaresGh1997/MLDM_HWs

Machine Learning and Data Mining Projects (2022-2023)

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MediaBilly/Image-Similarity

Comparison of multiple methods for calculating MNIST hand-written digits similarity.

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tabithaks/Capstone-GE-Asset-Tracking

Columbia University Data Science Master Capstone Project. The goal of this project was to cluster trajectories by shape for later optimization.

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slrbl/malicious-urls-detection-with-autoencoder-neural-networks

Detecting malicious URLs using an autoencoder neural network

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praveengadiyaram369/Activityrecognition_GaussianLDA

Gaussian Latent Dirichlet Allocation

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Swarno-Coder/AI-AutoEncoder

This repository represents an Auto-Encoder which can Encode and Decode itself and give the output at the output layer

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ettehadieh/Autoencoder-and-classifier-for-encoded-MNIST

In this program propose is making an autoencoder with Fully Connected Neural Networks and making a classifier to class encoded MNIST images

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azminewasi/Generative-Adversarial-Networks-Specialization-DeepLearning.ai

All course material and codes of Generative Adversarial Networks Specialization offered by DeepLearning.ai

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eyess-glitch/VAE-applied-to-fashion-mnist

Implementation of VAE (Variational Autoencoder) applied to the dataset fashion MNIST

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AniketP04/Credit-Card-Fraud-Detection

This project is used to detect a credit card fraud detection in an unsupervised manner. An autoencoder- based. an autoencoder with two hidden layer clustering model is build. an autoencoder with two hidden layer and K-means clustering unsupervised machine learning algorithm is used. The data has been taken from Kaggle

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toledoangel/automatic-image-brightness-adjustment

An automatic adjustment model is developed for brightness adjustment in images.

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surajh8596/AI-and-Deep-Learning

Artificial Neural Network and Deep Learning

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xxl4tomxu98/autoencoder-feature-extraction

Use auto encoder feature extraction to facilitate classification model prediction accuracy using gradient boosting models

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CAG9/Autoencoder-Feature-Extraction

Autoencoder for Feature Extraction

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kaledhoshme123/Colorize-Images-of-city-streets

Proposing a structure for a convolutional neural network capable of coloring grayscale images. The study focused on images of streets within cities. The generative neural network was trained on as many street images as possible.

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kyaiooiayk/Autoencoders-Notes

Notes, tutorials, code snippets and templates focused on Autoencoders for Machine Learning

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t0re199/DPNET_DEMO

Master's Thesis Project Demo

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UditSharma9999/Autoencoder

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afsanamimii/Anomaly_Detection_In_CCTV_Footages

This project detect anomalous event in CCTV footage. For the training purposes only normal events are used. When any violence or anomalous event happen the model can detect it.

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rbhubert/deep-learning-overview

Overview of different Deep Neural Network models using Tensorflow2 and Keras.

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VNDhanush/Satellite-Image-Enhancement

Image enhancement using GAN's and autoencoders

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xxl4tomxu98/convolutional-autoencoder-keras-tensorflow

Text Digit Character Computer Vision using convolutional autoencoder

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GiacomoLeoneMaria/A-brief-introduction-to-autoencoders

A gentle introduction to autoencoders with examples

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nafiul-araf/Anomaly-Detection

Anomaly detection (also known as outlier analysis) is a data mining step that detects data points, events, and/or observations that differ from the expected behavior of a dataset. A typical data might reveal significant situations, such as a technical fault, or prospective possibilities, such as a shift in consumer behavior.

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jczic/MicroNN

Micro neural network with multi-dimensional layers, multi-shaped data, fully or locally meshing, conv2D, unconv2D, Qlearning, ... for test!

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AdityaTheDev/ReconstructionOfImage-Using-DeepAutoEnccoders

Autoencoder is a type of neural network where the output layer has the same dimensionality as the input layer. In simpler words, the number of output units in the output layer is equal to the number of input units in the input layer. An autoencoder replicates the data from the input to the output in an unsupervised manner and is therefore sometimes referred to as a replicator neural network. The autoencoders reconstruct each dimension of the input by passing it through the network. It may seem trivial to use a neural network for the purpose of replicating the input, but during the replication process, the size of the input is reduced into its smaller representation. The middle layers of the neural network have a fewer number of units as compared to that of input or output layers. Therefore, the middle layers hold the reduced representation of the input. The output is reconstructed from this reduced representation of the input.

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rakibhhridoy/ImageDenoisingUsing-AutoEncoders

Filtering out the noise presented in the image by auto-enconder algorithm in TensorFow and Keras. Rare images, unclean crime images,medical noise images can be denoised and find out the desired outcome by using auto-encoders.

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ankit-a-aggarwal/ML_in_QF

AMS 691.03 Machine Learning in Quant Finance Project

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DevMilk/Gemerator

Gemerator is an autoencoder based mixed gem image generator, also it has a website and web service written in Django and Flask and deployed using PythonAnywhere and Google Cloud, Respectively

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Hardik2098/Paraphase-Generation-NLP

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