#Autoencoders
Showing 16 of 16 repositories tagged #autoencoders, ranked by stars
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
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
Scripts and notebooks to accompany the book Data-Driven Methods for Dynamic Systems
AutoKoopman - automated Koopman operator methods for data-driven dynamical systems analysis and control.
The scikit-learn-native foundation package for chemometrics 🧪 🤖
a novel architecture that leverages Autoencoders to superimpose the hidden representations of a base model and a fine-tuned model within a shared parameter space. Using B-spline-based blending coefficients and autoencoders that adaptively reconstruct the original hidden states based on the input data distribution.
Nvidia DLI workshop on AI-based anomaly detection techniques using GPU-accelerated XGBoost, deep learning-based autoencoders, and generative adversarial networks (GANs) and then implement and compare supervised and unsupervised learning techniques.
Code snippets and solutions for the Introduction to Deep Learning and Neural Networks Course hosted in educative.io
This is one of Petrobras' open repositories on GitHub. It contains the WPRAutoencoders project which encompasses a wellbore pressure response generator, a dataset of 20.000 synthetic pressure responses and an autoencoder neural network capable of clustering this data based on transmissibility and reservoir geometry.
This toolbox is support material for the book on CNN (http://www.convolution.network).
Intro to Deep Learning by National Research University Higher School of Economics
Lightweight and Fast Person Segmentation using Autoencoders (Trained Weights Included)
Statistics, signal processing, finance, econometrics, manufacturing, networking[disambiguation needed] and data mining, anomaly detection (also outlier detection) is the identification of rare items, events or observations which raise suspicions by differing significantly from the majority of the data. Typically the anomalous items will translate to some kind of problem such as bank fraud, a structural defect, medical problems or errors in a text. Anomalies are also referred to as outliers, novelties, noise, deviations and exceptions.