comet-examples
handson-ml
| comet-examples | handson-ml | |
|---|---|---|
| 2 | 1 | |
| 138 | 25,099 | |
| 3.6% | - | |
| 8.0 | 0.0 | |
| 18 days ago | 8 months ago | |
| Jupyter Notebook | Jupyter Notebook | |
| - | Apache License 2.0 |
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comet-examples
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MLOps level 4 tooling
- Quickstart guide
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[Discussion]Unlock the power of MLOps: A comprehensive guide to building a scalable and efficient ML pipeline
There's some good examples here. Pick your framework: https://github.com/comet-ml/comet-examples/blob/master/readme.md
handson-ml
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need a book recommendation for machine learning on python
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow is often recommended. You can check out the GitHub repo first: https://github.com/ageron/handson-ml
What are some alternatives?
you-dont-need-a-bigger-boat - An end-to-end implementation of intent prediction with Metaflow and other cool tools
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.
Hands-On-Deep-Learning-Algorithms-with-Python - Hands-On Deep Learning Algorithms with Python, By Packt
AeroPython - Classical Aerodynamics of potential flow using Python and Jupyter Notebooks
mlcomops
Machine-Learning-Specialization-Coursera - Contains Solutions and Notes for the Machine Learning Specialization By Stanford University and Deeplearning.ai - Coursera (2022) by Prof. Andrew NG
TensorFlow-Examples - TensorFlow Tutorial and Examples for Beginners (support TF v1 & v2)
ML-Workspace - 🛠 All-in-one web-based IDE specialized for machine learning and data science.
Twitter-sentiment-analysis - A sentiment analysis model trained with Kaggle GPU on 1.6M examples, used to make inferences on 220k tweets about Messi and draw insights from their results.
python-machine-learning-book-3rd-edition - The "Python Machine Learning (3rd edition)" book code repository
weightless_NN_decompression - Proof of concept for neural network decompression without storing any weights
embedding-encoder - Scikit-Learn compatible transformer that turns categorical variables into dense entity embeddings.