parsing-science / Pymc3_models
Licence: apache-2.0
Stars: ✭ 144
Labels
Projects that are alternatives of or similar to Pymc3 models
Visualizing cnns
Using Keras and cats to visualize layers from CNNs
Stars: ✭ 143 (-0.69%)
Mutual labels: jupyter-notebook
Elmo Tutorial
A short tutorial on Elmo training (Pre trained, Training on new data, Incremental training)
Stars: ✭ 145 (+0.69%)
Mutual labels: jupyter-notebook
Data Science Question Answer
A repo for data science related questions and answers
Stars: ✭ 2,000 (+1288.89%)
Mutual labels: jupyter-notebook
Dive Into Deep Learning Pytorch Pdf
本项目对中文版《动手学深度学习》中的代码进行了PyTorch实现并整理为PDF版本供下载
Stars: ✭ 144 (+0%)
Mutual labels: jupyter-notebook
Complete Python Bootcamp
Lectures for Udemy - Complete Python Bootcamp Course
Stars: ✭ 1,879 (+1204.86%)
Mutual labels: jupyter-notebook
Math Formula Recognition
Math formula recognition (Images to LaTeX strings)
Stars: ✭ 144 (+0%)
Mutual labels: jupyter-notebook
Face Recognition
Face recognition and its application as attendance system
Stars: ✭ 143 (-0.69%)
Mutual labels: jupyter-notebook
Tutorial Softweightsharingfornncompression
A tutorial on 'Soft weight-sharing for Neural Network compression' published at ICLR2017
Stars: ✭ 143 (-0.69%)
Mutual labels: jupyter-notebook
Pytorch tutorial
A set of jupyter notebooks on pytorch functions with examples
Stars: ✭ 142 (-1.39%)
Mutual labels: jupyter-notebook
Multihead Siamese Nets
Implementation of Siamese Neural Networks built upon multihead attention mechanism for text semantic similarity task.
Stars: ✭ 144 (+0%)
Mutual labels: jupyter-notebook
Alphatrading
An workflow in factor-based equity trading, including factor analysis and factor modeling. For well-established factor models, I implement APT model, BARRA's risk model and dynamic multi-factor model in this project.
Stars: ✭ 144 (+0%)
Mutual labels: jupyter-notebook
PyMC3 Models
Custom PyMC3 models built on top of the scikit-learn API. Check out the docs.
Features
- Reusable PyMC3 models including LinearRegression and HierarchicalLogisticRegression
- A base class, BayesianModel, for building your own PyMC3 models
Installation
The latest release of PyMC3 Models can be installed from PyPI using pip
:
pip install pymc3_models
The current development branch of PyMC3 Models can be installed from GitHub, also using pip
:
pip install git+https://github.com/parsing-science/pymc3_models.git
To run the package locally (in a virtual environment):
git clone https://github.com/parsing-science/pymc3_models.git
cd pymc3_models
virtualenv venv
source venv/bin/activate
pip install -r requirements.txt
Usage
Since PyMC3 Models is built on top of scikit-learn, you can use the same methods as with a scikit-learn model.
from pymc3_models import LinearRegression
LR = LinearRegression()
LR.fit(X, Y)
LR.predict(X)
LR.score(X, Y)
Contribute
For more info, see CONTRIBUTING.
Contributor Code of Conduct
Please note that this project is released with a Contributor Code of Conduct. By participating in this project you agree to abide by its terms. See CODE_OF_CONDUCT.
Acknowledgments
This library is built on top of PyMC3 and scikit-learn.
License
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].