autoencoder-neural-network

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Here are 69 public repositories matching this topic...

jbramburger / DataDrivenDynSyst

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

Updated Mar 16, 2025
Jupyter Notebook

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

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Detecting malicious URLs using an autoencoder neural network

intrusion-detection anomalydetection malware-classifier anomaly-detection enriched-data malware-classification autoencoder-neural-network malicious-urls-detection detect-intrusions

Updated Oct 9, 2022
Python

gd-vae / gd-vae

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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.

machine-learning deep-learning point-cloud neural-networks dynamical-systems manifold-learning dimension-reduction geometric-algorithms topological-data-analysis variational-autoencoder parameterized-systems geometric-deep-learning pytorch-implementation autoencoder-neural-network latent-spaces

Updated May 9, 2025
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Chinmayrane16 / DeepRecommender

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Training Deep AutoEncoders for Collaborative Filtering

deep-learning recommendation-system research-paper movielens-dataset state-of-the-art deep-autoencoders mmse pytorch-implementation autoencoder-neural-network

Updated Sep 9, 2019
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Greveley / AutoEncoderImageDenoise

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Using convolutional autoencoders to remove random noise from seismic data.

imageprocessing denoise seismic-data autoencoder-neural-network

Updated Jun 10, 2021
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ksasso1028 / audio-reverb-removal

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

audio dsp pytorch autoencoder convolutional-neural-networks time-domain denoising-autoencoders denoising multi-task-learning dereverberation autoencoder-neural-network demucs audio-denoising audio-machine-learning audio-ml audio-ai convtasnet

Updated Nov 30, 2023
Python

xxl4tomxu98 / autoencoder-feature-extraction

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

gradient-boosting-machine convolutional-autoencoder sequence-to-sequence variational-autoencoders autoencoder-neural-network autoencoder-classification autoencoderscompression xgboost-classifier light-gradient-boosting-machine autoencoder-denoising autoencoder-latent-layer histgram-gradient-boosting

Updated Apr 14, 2023
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jczic / MicroNN

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

python machine-learning reinforcement-learning qlearning deep-learning neural-network q-learning artificial-intelligence neural-networks neurons convolutional-neural-networks convolutional-autoencoder mlp-classifier autoencoder-neural-network jczic

Updated Jan 8, 2021
Python

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

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

python neural-network tensorflow pytorch generative-adversarial-network gan generative-model autoencoder generative-art autoencoders generative-models tensorflow2 autoencoder-neural-network

Updated Apr 29, 2023
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VNDhanush / Satellite-Image-Enhancement

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Image enhancement using GAN's and autoencoders

python gans autoencoder-neural-network

Updated Dec 8, 2022
Java

CAG9 / Autoencoder-Feature-Extraction

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Autoencoder for Feature Extraction

autoencoders self-supervised-learning tensorflow2 autoencoder-neural-network autoencoder-classification autoencoders-tensorflow

Updated Jun 1, 2022
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toledoangel / automatic-image-brightness-adjustment

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

python machine-learning computer-vision convolutional-neural-networks autoencoder-neural-network

Updated May 23, 2022
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nafiul-araf / Anomaly-Detection

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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.

visualization neural-network statistical-analysis outliers cnn-keras anomaly-detection zscore knn-classification local-outlier-factor one-class-svm iforest-model pyod autoencoder-neural-network inliers anomoly-score minimum-covariance

Updated Dec 19, 2021
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DevMilk / Gemerator

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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

deep-neural-networks deep-learning image-generation autoencoders gemstone autoencoder-neural-network

Updated May 26, 2022
JavaScript

VaishnaviKrishna / bias-field-correction

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

deep-neural-networks python3 neuroimaging autoencoder-neural-network

Updated Sep 14, 2019
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praveengadiyaram369 / Activityrecognition_GaussianLDA

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Gaussian Latent Dirichlet Allocation

Updated Jan 26, 2022
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nafiul-araf / BFND-System

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

hashing machine-learning deep-learning hybrid-model ensemble-machine-learning autoencoder-neural-network bnlp