Posts tagged AI/ML

Citrate Part 1: My First Rust Project

In this blog post, I want to announce Cerebral a framework written in Rust for experimenting with Self-organizing Neural Networks such as the famous Kohonen networks. It is designed in a way that a network can be defined by composing a model out of a variety of algorithm that define its behavior. Unlike deep learning architectures, the behavior is not defined by a composition of layers, loss functions and diverse learning methods. This type of network defines a less hierarchical topology and learning rules affect all neurons at once. Therefore, this project has different goals as prominent deep learning frameworks, and thus, a different architecture. Being my first Rust library (applications are simpler to implement), I had to evaluate a few options to achieve my goal and I often failed enough battling with Rust’s occasionally frustrating type system.

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Announcing the Citrate workspace

Data science with rust! Finally, two years after having started to learn and develop in the Rust programming language, I am happy to find the time to write about my two projects that I have been developing in my rare spare time during the last year.

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AI/ML Essentials part 3: Gaussian Process Regression

In this article in the AI/ML Essentials series, we will learn about yet another machine learning model—Gaussian Process Regression. In the previous articles, we encountered instances of unsupervised learning (for clustering and density estimation). The last issue presented a simple taxonomy of machine learning, which guides us through the series:

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AI/ML Essentials Part 2: Cluster analysis with (Gaussian) Mixture Models

This is the second part of the series “AI/ML Essentials”, which intends to be a gentle introduction to the topic using mostly layman’s terms and as little mathematics as possible–you don’t need an engineering degree to see the beauty within this fascinating field.

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AI/ML Essentials Part1: Self-Organizing Maps

This article is the start of a series that introduces different models and algorithms to beginners

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