AeroPython
homemade-machine-learning
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| AeroPython | homemade-machine-learning | |
|---|---|---|
| 3 | 7 | |
| 892 | 22,493 | |
| 1.2% | - | |
| 0.0 | 3.8 | |
| almost 4 years ago | 9 months ago | |
| Jupyter Notebook | Jupyter Notebook | |
| GNU General Public License v3.0 or later | MIT License |
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AeroPython
homemade-machine-learning
- Homemade Machine Learning
-
✨ 5 Best GitHub Repositories to Learn Machine Learning in 2022 for Free 💯
4️⃣ Homemade Machine Learning
- Python examples of popular machine learning algorithms with interactive Jupyter demos and math being explained
- Homemade Machine Learning: Python examples of popular machine learning algorithms
- Homemade Machine Learning: Python examples of popular machine learning algorithms with interactive Jupyter demos
What are some alternatives?
AeroSandbox - Aircraft design optimization made fast through modern automatic differentiation. Composable analysis tools for aerodynamics, propulsion, structures, trajectory design, and much more.
lego-mindstorms - My LEGO MINDSTORMS projects (using set 51515 electronics)
CFDPython - A sequence of Jupyter notebooks featuring the "12 Steps to Navier-Stokes" http://lorenabarba.com/
wordle-solver - For educational purposes, a simple script that assists in solving the word game Wordle.
py - Repository to store sample python programs for python learning
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
handson-ml - ⛔️ DEPRECATED – See https://github.com/ageron/handson-ml3 instead.
rmi - A learned index structure
PythonDataScienceHandbook - Python Data Science Handbook: full text in Jupyter Notebooks
PythonRobotics - Python sample codes for robotics algorithms.
open-space-toolkit - Collection of versatile software libraries for space engineering applications.
the-elements-of-statistical-learning - My notes and codes (jupyter notebooks) for the "The Elements of Statistical Learning" by Trevor Hastie, Robert Tibshirani and Jerome Friedman