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 www.oranlooney.com
ML From Scratch, Part 5: Gaussian Mixture Models
| | 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.
| | sebastianraschka.com
Predictive modeling, supervised machine learning, and pattern classification
1.4 parsecs away

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| | I'm Sebastian: a machine learning & AI researcher, programmer, and author. As Staff Research Engineer Lightning AI, I focus on the intersection of AI research, software development, and large language models (LLMs).
| | blog.quipu-strands.com
Bayesian Optimization, Part 2: Acquisition Functions
2.6 parsecs away

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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.
| | blog.scottlogic.com
The basics of the Layered Neural Network
17.5 parsecs away

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| Recently I've been learning about Neural Networks and how they work. In this blog post I write a simple introduction in to some of the core concepts of a basic layered neural network.