indaba-pracs-2022 VS bodywork-pymc3-project

Compare indaba-pracs-2022 vs bodywork-pymc3-project and see what are their differences.

bodywork-pymc3-project

Serving Uncertainty with Bayesian inference, using PyMC3 with Bodywork (by bodywork-ml)
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indaba-pracs-2022 bodywork-pymc3-project
1 1
172 13
0.6% -
0.0 5.3
29 days ago almost 2 years ago
Jupyter Notebook Jupyter Notebook
Apache License 2.0 MIT License
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
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For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.

indaba-pracs-2022

Posts with mentions or reviews of indaba-pracs-2022. We have used some of these posts to build our list of alternatives and similar projects.

bodywork-pymc3-project

Posts with mentions or reviews of bodywork-pymc3-project. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2021-05-17.

What are some alternatives?

When comparing indaba-pracs-2022 and bodywork-pymc3-project you can also consider the following projects:

PyCBC-Tutorials - Learn how to use PyCBC to analyze gravitational-wave data and do parameter inference.

bodywork-pipeline-with-aporia-monitoring - Integrating Aporia ML model monitoring into a Bodywork serving pipeline.

neural-tangents - Fast and Easy Infinite Neural Networks in Python

VevestaX - 2 Lines of code to track ML experiments + EDA + check into Github

jaxrl - JAX (Flax) implementation of algorithms for Deep Reinforcement Learning with continuous action spaces.

amazon-sagemaker-examples - Example 📓 Jupyter notebooks that demonstrate how to build, train, and deploy machine learning models using 🧠 Amazon SageMaker.

pymc-resources - PyMC educational resources

bodywork - ML pipeline orchestration and model deployments on Kubernetes.

brax - Massively parallel rigidbody physics simulation on accelerator hardware.

H2O - H2O is an Open Source, Distributed, Fast & Scalable Machine Learning Platform: Deep Learning, Gradient Boosting (GBM) & XGBoost, Random Forest, Generalized Linear Modeling (GLM with Elastic Net), K-Means, PCA, Generalized Additive Models (GAM), RuleFit, Support Vector Machine (SVM), Stacked Ensembles, Automatic Machine Learning (AutoML), etc.

nn - 🧑‍🏫 60 Implementations/tutorials of deep learning papers with side-by-side notes 📝; including transformers (original, xl, switch, feedback, vit, ...), optimizers (adam, adabelief, sophia, ...), gans(cyclegan, stylegan2, ...), 🎮 reinforcement learning (ppo, dqn), capsnet, distillation, ... 🧠

whylogs-examples - A collection of WhyLogs examples in various languages