Understanding_the_EM_Algorithm
handson-ml
| Understanding_the_EM_Algorithm | handson-ml | |
|---|---|---|
| 1 | 1 | |
| 7 | 25,094 | |
| - | - | |
| 0.0 | 0.0 | |
| about 2 years ago | 7 months ago | |
| Jupyter Notebook | Jupyter Notebook | |
| MIT License | Apache License 2.0 |
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Understanding_the_EM_Algorithm
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[D] My new blog post "Understanding the EM Algorithm"
The EM algorithm is very straightforward to understand with one or two proof-of-concept examples. However, if you really want to understand how it works, it may take a while to walk through the math. The purpose of this article is to establish a good intuition for you, while also provide the mathematical proofs for interested readers. The codes for all the examples mentioned in this article can be found at https://github.com/mistylight/Understanding_the_EM_Algorithm.
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?
azureml-examples - Official community-driven Azure Machine Learning examples, tested with GitHub Actions.
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.
ML-For-Beginners - 12 weeks, 26 lessons, 52 quizzes, classic Machine Learning for all
AeroPython - Classical Aerodynamics of potential flow using Python and Jupyter Notebooks
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
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.
tensorflow - An Open Source Machine Learning Framework for Everyone
musgo - [Deprecated] Provides serialization with validation support for Golang