arogozhnikov / Hep_ml
Licence: other
Machine Learning for High Energy Physics.
Stars: ✭ 133
Programming Languages
python
139335 projects - #7 most used programming language
Projects that are alternatives of or similar to Hep ml
Ml Workspace
🛠 All-in-one web-based IDE specialized for machine learning and data science.
Stars: ✭ 2,337 (+1657.14%)
Mutual labels: jupyter-notebook, neural-networks, scikit-learn
Hands On Machine Learning With Scikit Learn Keras And Tensorflow
Notes & exercise solutions of Part I from the book: "Hands-On ML with Scikit-Learn, Keras & TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems" by Aurelien Geron
Stars: ✭ 151 (+13.53%)
Mutual labels: jupyter-notebook, neural-networks, scikit-learn
Deep learning projects
Stars: ✭ 28 (-78.95%)
Mutual labels: jupyter-notebook, neural-networks, scikit-learn
Sigmoidal ai
Tutoriais de Python, Data Science, Machine Learning e Deep Learning - Sigmoidal
Stars: ✭ 103 (-22.56%)
Mutual labels: jupyter-notebook, neural-networks
Pymc Example Project
Example PyMC3 project for performing Bayesian data analysis using a probabilistic programming approach to machine learning.
Stars: ✭ 90 (-32.33%)
Mutual labels: jupyter-notebook, scikit-learn
Neural Tangents
Fast and Easy Infinite Neural Networks in Python
Stars: ✭ 1,357 (+920.3%)
Mutual labels: jupyter-notebook, neural-networks
Scikit Learn Tips
🤖⚡️ scikit-learn tips
Stars: ✭ 1,203 (+804.51%)
Mutual labels: jupyter-notebook, scikit-learn
Dtreeviz
A python library for decision tree visualization and model interpretation.
Stars: ✭ 1,857 (+1296.24%)
Mutual labels: jupyter-notebook, scikit-learn
Pytorchnlpbook
Code and data accompanying Natural Language Processing with PyTorch published by O'Reilly Media https://nlproc.info
Stars: ✭ 1,390 (+945.11%)
Mutual labels: jupyter-notebook, neural-networks
A Nice Mc
Code for "A-NICE-MC: Adversarial Training for MCMC"
Stars: ✭ 115 (-13.53%)
Mutual labels: jupyter-notebook, neural-networks
Pbpython
Code, Notebooks and Examples from Practical Business Python
Stars: ✭ 1,724 (+1196.24%)
Mutual labels: jupyter-notebook, scikit-learn
Credit Risk Modelling
Credit Risk analysis by using Python and ML
Stars: ✭ 91 (-31.58%)
Mutual labels: jupyter-notebook, scikit-learn
Knet.jl
Koç University deep learning framework.
Stars: ✭ 1,260 (+847.37%)
Mutual labels: jupyter-notebook, neural-networks
Codesearchnet
Datasets, tools, and benchmarks for representation learning of code.
Stars: ✭ 1,378 (+936.09%)
Mutual labels: jupyter-notebook, neural-networks
Neural Networks
brief introduction to Python for neural networks
Stars: ✭ 82 (-38.35%)
Mutual labels: jupyter-notebook, neural-networks
Selfdrivingcar
A collection of all projects pertaining to different layers in the SDC software stack
Stars: ✭ 107 (-19.55%)
Mutual labels: jupyter-notebook, scikit-learn
Python Machine Learning Zh
Python机器学习,机器学习入门首选。
Stars: ✭ 117 (-12.03%)
Mutual labels: jupyter-notebook, scikit-learn
Autoencoders
Implementation of simple autoencoders networks with Keras
Stars: ✭ 123 (-7.52%)
Mutual labels: jupyter-notebook, neural-networks
Dive Into Machine Learning
Dive into Machine Learning with Python Jupyter notebook and scikit-learn! First posted in 2016, maintained as of 2021. Pull requests welcome.
Stars: ✭ 10,810 (+8027.82%)
Mutual labels: jupyter-notebook, scikit-learn
Math And Ml Notes
Books, papers and links to latest research in ML/AI
Stars: ✭ 76 (-42.86%)
Mutual labels: jupyter-notebook, neural-networks
hep_ml
hep_ml provides specific machine learning tools for purposes of high energy physics.
Main features
- uniform classifiers - the classifiers with low correlation of predictions and mass (or some other variable, or even set of variables)
- uBoost optimized implementation inside
- UGradientBoosting (with different losses, specially FlatnessLoss is of high interest)
- measures of uniformity (see hep_ml.metrics)
- advanced losses for classification, regression and ranking for UGradientBoosting (see hep_ml.losses).
- hep_ml.nnet - theano-based flexible neural networks
-
hep_ml.reweight - reweighting multidimensional distributions
(multi here means 2, 3, 5 and more dimensions - see GBReweighter!) - hep_ml.splot - minimalistic sPlot-ting
- hep_ml.speedup - building models for fast classification (Bonsai BDT)
- sklearn-compatibility of estimators.
Installation
Basic installation:
pip install hep_ml
If you're new to python and never used pip
, first install scikit-learn with these instructions.
To use latest development version, clone it and install with pip
:
git clone https://github.com/arogozhnikov/hep_ml.git
cd hep_ml
pip install .
Local testing:
nosetests tests/
Links
- documentation
-
notebooks, code examples
- you may need to install
ROOT
androot_numpy
to run those
- you may need to install
- repository
- issue tracker
Related projects
Libraries you'll require to make your life easier and HEPpier.
- IPython Notebook — web-shell for python
- scikit-learn — general-purpose library for machine learning in python
- numpy — 'MATLAB in python', vector operation in python. Use it you need to perform any number crunching.
- theano — optimized vector analytical math engine in python
- ROOT — main data format in high energy physics
- root_numpy — python library to deal with ROOT files (without pain)
License
Apache 2.0, hep_ml
is an open-source library.
Platforms
Linux, Mac OS X and Windows are supported.
hep_ml supports both python 2 and python 3.
Note that the project description data, including the texts, logos, images, and/or trademarks,
for each open source project belongs to its rightful owner.
If you wish to add or remove any projects, please contact us at [email protected].