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 blog.quipu-strands.com
Bayesian Optimization, Part 2: Acquisition Functions
| | www.oranlooney.com
ML From Scratch, Part 5: Gaussian Mixture Models
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| | [AI summary] The article discusses unsupervised learning, focusing on the Gaussian Mixture Model (GMM) and the Expectation-Maximization (EM) algorithm. It explains how GMM uses the EM algorithm to cluster data without labeled examples, and demonstrates its application on the Iris dataset. While GMM successfully finds clusters, the agreement with true labels is only 96% for one random seed, with variability across trials. The article highlights the limitations of unsupervised learning, including arbitrary complexity parameters, lack of hard metrics, and subjective model interpretation. It concludes that unsupervised learning is valuable for exploratory analysis and representation learning but requires more expertise and domain input compared to supervised met...
| | distill.pub
Exploring Bayesian Optimization
0.3 parsecs away

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| | How to tune hyperparameters for your machine learning model using Bayesian optimization.
| | blog.ml.cmu.edu
How to Regularize Your Regression - Machine Learning Blog | ML@CMU | Carnegie Mellon University
2.8 parsecs away

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| | The latest news and publications regarding machine learning, artificial intelligence or related, brought to you by the Machine Learning Blog, a spinoff of the Machine Learning Department at Carnegie Mellon University.
| | futurism.com
Artificial Intelligence: What It Is and How It Really Works
20.4 parsecs away

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| A primer on AI: what it is, how it works, and why it's important.