mango
machine_learning_basics
| mango | machine_learning_basics | |
|---|---|---|
| - | 5 | |
| 310 | 4,205 | |
| 1.0% | - | |
| 5.8 | 0.0 | |
| about 2 months ago | 3 months ago | |
| Jupyter Notebook | Jupyter Notebook | |
| Apache License 2.0 | MIT License |
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mango
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Tracking mentions began in Dec 2020.
machine_learning_basics
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Bayesian linear regression in (plain) Python
A while back I open sourced a repository implementing fundamental machine learning algorithms in Python, along with the most important theoretical information. I originally created the repository for myself when preparing for AI residency interviews. You can find the original Reddit post here.
- Bayesian linear regression in Python
What are some alternatives?
Hyperactive - An optimization and data collection toolbox for convenient and fast prototyping of computationally expensive models.
Financial-Models-Numerical-Methods - Collection of notebooks about quantitative finance, with interactive python code.
vizier - Python-based research interface for blackbox and hyperparameter optimization, based on the internal Google Vizier Service.
100-Days-Of-ML-Code - 100 Days of ML Coding
neural-tangents - Fast and Easy Infinite Neural Networks in Python
borb-google-colab-examples - This repository contains some examples of using borb in google colab. These examples enable you to try out the features of borb without installing it on your system. They also ensure the system requirements and imports are all taken care of.
Bayesian-Optimization-in-FSharp - Bayesian Optimization via Gaussian Processes in F#
trulens - Evaluation and Tracking for LLM Experiments
Spotify_Song_Recommender - This project leverages spotify's api and provided user playlists to create and tune a neural network model that generates song recommendations based off of song data in provided playlists.
rmi - A learned index structure
nni - An open source AutoML toolkit for automate machine learning lifecycle, including feature engineering, neural architecture search, model compression and hyper-parameter tuning.
PyImpetus - PyImpetus is a Markov Blanket based feature subset selection algorithm that considers features both separately and together as a group in order to provide not just the best set of features but also the best combination of features