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ML From Scratch, Part 5: Gaussian Mixture Models
| | blog.quipu-strands.com
Bayesian Optimization, Part 2: Acquisition Functions
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| | [AI summary] The text presents an extensive overview of Bayesian optimization techniques, focusing on their applications in black-box function optimization, including challenges and solutions such as computational efficiency, scalability, and integration with deep learning models. It also highlights key research contributions and references to seminal papers and authors in the field.
| | iamirmasoud.com
Machine Learning Interview: Classical Algorithms
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| | Amir Masoud Sefidian
| | blog.keras.io
Building Autoencoders in Keras
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| | [AI summary] The text discusses various types of autoencoders and their applications. It starts with basic autoencoders, then moves to sparse autoencoders, deep autoencoders, and sequence-to-sequence autoencoders. The text also covers variational autoencoders (VAEs), explaining their structure and training process. It includes code examples for each type of autoencoder and mentions the use of tools like TensorBoard for visualization. The VAE section highlights how to generate new data samples and visualize the latent space. The text concludes with references and a note about the potential for further topics.
| | aakinshin.net
Quantile-respectful density estimation based on the Harrell-Davis quantile estimator
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| The idea of this post was born when I was working on a presentation for my recent DotNext talk. It had a slide with a density plot like this: Here we can see a density plot based on a sample with highlighted decile locations that split the plot into 10 equa...