Songyosk

Songyosk's Stars

• aymericdamien/TensorFlow-Examples

  TensorFlow Tutorial and Examples for Beginners (support TF v1 & v2)

  Language:Jupyter Notebook43.4k2k23514.9k

• shap/shap

  A game theoretic approach to explain the output of any machine learning model.

  Language:Jupyter Notebook22.9k2442.5k3.3k

• recommenders-team/recommenders

  Best Practices on Recommendation Systems

  Language:Python19.3k2758693.1k

• ritchieng/the-incredible-pytorch

  The Incredible PyTorch: a curated list of tutorials, papers, projects, communities and more relating to PyTorch.

  11.5k471232.1k

• interpretml/interpret

  Fit interpretable models. Explain blackbox machine learning.

  Language:C++6.3k145450729

• deepchem/deepchem

  Democratizing Deep-Learning for Drug Discovery, Quantum Chemistry, Materials Science and Biology

  Language:Python5.5k1431.7k1.7k

• man-group/arctic

  High performance datastore for time series and tick data

  Language:Python3.1k173556584

• rdkit/rdkit

  The official sources for the RDKit library

  Language:HTML2.7k793.3k881

• krasserm/bayesian-machine-learning

  Notebooks about Bayesian methods for machine learning

  Language:Jupyter Notebook1.8k7618461

• hsiaoyi0504/awesome-cheminformatics

  A curated list of Cheminformatics libraries and software.

  6974944115

• txie-93/cgcnn

  Crystal graph convolutional neural networks for predicting material properties.

  Language:Python6542338313

• SINGROUP/dscribe

  DScribe is a python package for creating machine learning descriptors for atomistic systems.

  Language:C++400189989

• pyiron/pyiron

  pyiron - an integrated development environment (IDE) for computational materials science.

  Language:Jupyter Notebook3651633549

• txie-93/cdvae

  An SE(3)-invariant autoencoder for generating the periodic structure of materials [ICLR 2022]

  Language:Python24235694

• krishnaik06/Recommendation_complete_tutorial

  Language:Jupyter Notebook16463260

• ropensci/webchem

  Chemical Information from the Web

  Language:R1611922041

• by256/icsg3d

  3-D Inorganic Crystal Structure Generation and Property Prediction via Representation Learning (JCIM 2020)

  Language:Python373512

• Chinmayrane16/DeepRecommender

  Training Deep AutoEncoders for Collaborative Filtering

  Language:Jupyter Notebook22413

• ifyoungnet/ChemDes

  An integrated web-based platform for molecular descriptor and fingerprint computation

  11214

• Songyosk/UVVIS

  Automatic Prediction of Peak Optical Absorption Wavelengths in Molecules using Convolutional Neural Networks

  Language:Python10202

• Songyosk/GBFS4MPPML

  Official implementation of "Gradient Boosted and Statistical Feature Selection Pipeline for Materials Property Predictions"

  Language:Jupyter Notebook8100

• Songyosk/BGML

  Automatic Prediction of Band Gaps of Inorganic Materials using Machine Learning

  Language:Python5211

• Songyosk/CurieML

  Prediction of Curie Temperature using Machine Learning

  Language:Python50

• Songyosk/CCNW4RMC

  Official implementation of "Current-controlled nanomagnetic writing for reconfigurable magnonic crystals"

  3100

• Songyosk/RayTracer

  Computational modelling of ray propagation through optical elements using the principles of geometric optics (Ray Tracer)

  Language:Python3101

• Songyosk/MNLL2ELDM

  Minimisation of a negative log likelihood fit to extract the lifetime of the D^0 meson (MNLL2ELDM)

  Language:Python2100

• Songyosk/MSDPS

  Modelling Single & Double Pendulum Systems (MSDPS) - Using finite difference methods for solving ordinary differential equations

  Language:Jupyter Notebook2100

• Songyosk/PM

  Percolation Modelling (PM)

  Language:Jupyter Notebook2100

• Songyosk/SuperconductivityML

  Machine-Learning Predictions of Critical Temperatures from Chemical Compositions of Superconductors

  Language:Python2100