Popular GitHub Repositories for Concrete Autoencoders

A Collection of Variational Autoencoders (VAE) in PyTorch.

[CVPR2020] Adversarial Latent Autoencoders

Jupyter Notebook tutorials on solving real-world problems with Machine Learning & Deep Learning using PyTorch. Topics: Face detection with Detectron 2, Time Series anomaly detection with LSTM Autoencoders, Object Detection with YOLO v5, Build your first Neural Network, Time Series forecasting for Coronavirus daily cases, Sentiment Analysis with BER

Deep learning for recommender systems

Concrete ML: Privacy Preserving ML framework using Fully Homomorphic Encryption (FHE), built on top of Concrete, with bindings to traditional ML frameworks.

My first Python repo with codes in Machine Learning, NLP and Deep Learning with Keras and Theano

TensorFlow 101: Introduction to Deep Learning

Machine Learning tutorials with TensorFlow 2 and Keras in Python (Jupyter notebooks included) - (LSTMs, Hyperameter tuning, Data preprocessing, Bias-variance tradeoff, Anomaly Detection, Autoencoders, Time Series Forecasting, Object Detection, Sentiment Analysis, Intent Recognition with BERT)

TorchXRayVision: A library of chest X-ray datasets and models. Classifiers, segmentation, and autoencoders.

Code for Hands-on Unsupervised Learning Using Python (O'Reilly Media)

[CVPR 2023] VideoMAE V2: Scaling Video Masked Autoencoders with Dual Masking

A complete end-to-end pipeline for LLM interpretability with sparse autoencoders (SAEs) using Llama 3.2, written in pure PyTorch and fully reproducible.

A U-Net combined with a variational auto-encoder that is able to learn conditional distributions over semantic segmentations.

iPython notebook and pre-trained model that shows how to build deep Autoencoder in Keras for Anomaly Detection in credit card transactions data

GraphMAE: Self-Supervised Masked Graph Autoencoders in KDD'22

Bayesian Deep Learning: A Survey

ConvMAE: Masked Convolution Meets Masked Autoencoders

Tensorflow implementation of variational auto-encoder for MNIST

[Pytorch] Generative retrieval model using semantic IDs from "Recommender Systems with Generative Retrieval"

Easy generative modeling in PyTorch