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)

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

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

PyTorch Implementations For A Series Of Deep Learning-Based Recommendation Models

Place recognition with WiFi fingerprints using Autoencoders and Neural Networks

Autoencoders for Link Prediction and Semi-Supervised Node Classification (DSAA 2018)

Implementation of simple autoencoders networks with Keras

Network-to-Network Translation with Conditional Invertible Neural Networks

Hiding Images within other images using Deep Learning

A tensorflow.keras generative neural network for de novo drug design, first-authored in Nature Machine Intelligence while working at AstraZeneca.

This repository explores the variety of techniques and algorithms commonly used in deep learning and the implementation in MATLAB and PYTHON

Compressive Autoencoder.

Collection of operational time series ML models and tools

Gradient Origin Networks - a new type of generative model that is able to quickly learn a latent representation without an encoder

[ICCV 2023 Oral] Official Implementation of "Denoising Diffusion Autoencoders are Unified Self-supervised Learners"

Deep Learning-based Clustering Approaches for Bioinformatics

🧶 Modular VAE disentanglement framework for python built with PyTorch Lightning ▸ Including metrics and datasets ▸ With strongly supervised, weakly supervised and unsupervised methods ▸ Easily configured and run with Hydra config ▸ Inspired by disentanglement_lib

The code for the MaD TwinNet. Demo page:

Language Quantized AutoEncoders