Learn how to test with tape! (tap-producing test harness for node)
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Panoptic segmentation algorithms for 2D and 3D electron microscopy images
- deep-learning
- semantic-segmentation
- segmentation
- pytorch
The repository host gives no description.
- rubyml
- ruby
- python
- scikit-learn
Tensorflow/Keras Plug-N-Play Deep Learning Models Compilation
Header-only C++11 library using OpenCV for high-throughput image-based plant phenotyping
- plant-phenotyping
- opencv
- charuco
- color-correction
DataSet File Format (DSFF)
- dataset
- file-format
- machine-learning
- python
The repository host gives no description.
The repository host gives no description.
A curated list for resources on Neuraxle.
The repository not only provides users who are starting out with Content Hub ONE, Sitecore's pure headless CMS, with the assets they need to showcase the product, but also allows them to learn how it was built from a real world example.
Machine learning model evaluation made easy: plots, tables, HTML reports, experiment tracking and Jupyter notebook analysis.
A Framework for Encrypted Machine Learning in TensorFlow
A Single Library Parser to extract meta information,static analysis and detect macros within the files.
- malware-analysis
- pdf-parsing
- pe-executable
- office-files
library for nlp with ruby
- rubyml
- nlp
A text classification iOS app using tensorflow lite
- ios
- swift
- tensorflow-lite
- tensorflow
A neural networks library for Clojure
A Julia wrapper for the Smile C++ Structural Modeling, Inference, and Learning Engine for Bayesian & Influence Networks
See and learn how the CAAnimation and CAMediaTiming properties affect rendering using dynamic Xcode playground.
- core-animation
- swift-playground
- xcode-playground
- macos
Model your data with machine learning
- rubyml
- ml
- machine-learning
- machinelearning
A column-oriented approach to feature engineering. Feature engineering and machine learning: together at last!
Comparing the performance of Stacked Ensemble Learning & machine learning algorithms like Random Forest, Decision Tree, Adaboost, Gradient Boost and XGBoost Classifier in Python for Stock Market Trend Prediction.
GNN-based gene-selection deep learning model for drug response prediction
Estimating the number of clusters in a data set via the gap statistic. Implemented in H2O-3
- kmeans
- kmeans-clustering
- clustering
- gap-statistic
Implementation of Partitioning Around Medoids (PAM) in H2O-3
- clustering
- machine-learning
- h2o-3
- java