Conditional normalizing flows (NFs), conditional GANs, and conditional variational autoencoders (CVAEs) with sklearn-like interface

sklearn wrappers for stacked denoising autoencoders

Denoising Autoencoder wrapper (from Theano) to sklearn (scikit learn)

2021 Python, Deep Archetypal Analysis (Bayesian NN, Deep Variational Autoencoder), Compressed Sensing, Kmeans, setuptools, tensorflow (keras), tensorflow_probability, sklearn (cluster, metrics, model_selection, preprocessing), pandas, typing

End-to-end ML pipeline for network intrusion detection on CIC-IDS2017. Features data cleaning, EDA, feature engineering, PCA, multi-class/binary classification (RF, XGBoost), and unsupervised methods (Isolation Forest, Autoencoder). Built with Python, Sklearn, TF/Keras.

Python, Pandas, Sklearn, Matplotlib, Seaborn, Keras, TensorFlow Creates customer groups for targeted ad campaigns based on their buying habits, purchase history, credit history Developed using K-means clustering, autoencoders and visualized busting heatmap, subplot-bar charts, scatterplots, and KDE

An pytorch implementation of Deep Belief Network with sklearn compatibility for classification. The training process consists the pretraining of DBN, fine-tuning as an unrolled autoencoder-decoder, and supervised fine-tuning as a classifier.

Autoencoder in Sklearn

Baseline scikit-learn autoencoder for anomaly detection in elderly home surveillance, using per-person image datasets.

Sklearn wrapper for EASE - Embarrassingly Shallow Autoencoders (Harald Steck)

neural networks and autoencoders (CODE) [ PYTHON - TENSORFLOW / SKLEARN / PANDAS / NUMPY ]

Create PyTorch-based MLP networks/autoencoders with the simplicity of the sklearn interface

A memory-efficient, sklearn-object re-write of R. Camino's Variational AutoEncoder Imputer.

Experimenting with MNIST dataset using Neural Networks (FC, CNN), Autoencoder architecture and SKLearn classifiers

Includes drug discovery project, ECG autoencoders, life expectancy regression models, and intro ML coursework. Uses pd, np, plt, sns, sklearn, keras.

A project that (1) applies a convolutional autoencoder to extract a reduced number of critical features of signals and (2) tests three machine learning classification methods, using Keras and Sklearn.