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machine_learning

Some fundamental machine learning and data-analysis techniques are revisited here.

machine_learning-classification_regression_and_classifier_interpretability

1. Classification with Hyperparameter Search : The idea here is to train and evaluate 8 classification methods across 10 classification datasets. 2. Regression with Hyperparameters Search: The idea here is to train and evaluate 7 regression methods across 10 regression datasets. 3. Classifier interpretability : load and train models on standard computer vision dataset called CIFAR-10 and train a convolutional neural network using PyTorch to classify images in the dataset; train a decision tree to classify images in the dataset; and try to interpret the CNN using the 'activation maximization' technique. 4. Novelty component : Try to introduce a novel aspect to your analysis of classifiers and regressors or to your investigation of interpretability.

ml-iter-additive

An iterative machine learning framework for predicting temperature profiles for an additive manufacturing process

moving-average

A python library for time-series smoothing and outlier detection in a vectorized way.

neural-koopman-lyapunov-control

Neural Koopman Lyapunov Control

neural_koopman_pooling

[CVPR 2023] Neural Koopman Pooling: Control-Inspired Temporal Dynamics Encoding for Skeleton-Based Action Recognition

pidl

poreanalyser

PoreAnalyzer - automated rapid analysis and classification of defects in additive manufacturing processes, such as LPBF

predictions-of-thermal-fields-in-additive-manufacturing

Predicting Thermal Fields in AdditiveManufacturing by FEM simulations andMachine Learning

pytorch-beginner

pytorch tutorial for beginners

pytorch-pdqn-for-digital-twin-acs

PyTorch implementation of RIC for conveyor systems with Deep Q-Networks (DQN) and Profit-Sharing (PS). Wang, T., Cheng, J., Yang, Y., Esposito, C., Snoussi, H., & Tao, F. (2020). Adaptive Optimization Method in Digital Twin Conveyor Systems via Range-Inspection Control. IEEE Transactions on Automation Science and Engineering.

pytorch-vae

A Collection of Variational Autoencoders (VAE) in PyTorch.

pytorch-vq-vae

PyTorch implementation of VQ-VAE by Aäron van den Oord et al.

real-world-anomaly-detection-pytorch

PyTorch Implementation of Real-world Anomaly Detection in Surveillance Videos (CVPR '17)

scrae

Code for scRAE: Deterministic Regularized Autoencoders with Flexible Priors for Clustering Single-cell Gene Expression Data

semi-supervised-learning-for-acoustic-impedance-inversion

Codes and data for the paper: M. Alfarraj and G. AlRegib, "Semi-Supervised Learning for Acoustic Impedance Inversion," in Expanded Abstracts of the SEG Annual Meeting, Sep. 15-20 2019.

signal-processing

supervised_learning-scikit-learn

tribology-lstm-encoder_decoder

Long short-term memory based semi-supervised encoder-decoder for early prediction of failures in self-lubricating bearings

udkm1dsim

A Python Simulation Toolkit for 1D Ultrafast Dynamics in Condensed Matter