A project detailing the creation of a single board computer based on the Fairchild F8 microprocessor. The article describes the initial build, testing, and documentation process. Build files and documentation are available for download.
A new study by Google DeepMind explores whether artificial intelligence can exhibit genuine creativity through the composition of chess puzzles. Experts evaluated the AI-generated compositions, noting both positive aspects and areas for improvement.
A hands-on introduction to Futhark through a collection of commented programs, listed in roughly increasing order of complexity.
This post explores a new idea for parallelizing a simplified parsing task using a "stack monoid" and scan operations, potentially enabling efficient GPU implementation of parsing algorithms.
This article explores the challenges and possibilities of writing portable and efficient SIMD code in Rust, aiming for a "fearless SIMD" approach with high-level, safe, and composable primitives.
This Gist contains C++ code demonstrating the use of vpternlogd (AVX-512) for ternary logic operations. The code includes implementations for random bit generation, string to bit conversion, and functions to perform ternary logic using AVX-512 intrinsics.
A mini review of the GL.iNet Comet GL-RM1, a remote KVM device that allows remote control of systems without onboard iKVM. The review covers hardware overview, setup and potential use cases, comparing it to alternatives like the JetKVM.
This book provides an introductory, textbook-like treatment of multi-armed bandits. It covers various algorithms and techniques for decision-making under uncertainty, with a focus on theoretical foundations and practical applications.
* **Multi-Armed Bandit Framework:** The document introduces the core concept of multi-armed bandits – a model for decision-making under uncertainty, often used as a simplified starting point for more complex reinforcement learning problems.
* **Applications:** It highlights several applications, including news website optimization, dynamic pricing, and medical trials.
* **Key Concepts:** Defines crucial concepts like arms, rewards, regret, exploration vs. exploitation, and different feedback mechanisms (bandit, full, partial).
* **Algorithms:** Presents and analyzes simple algorithms like Explore-First and Epsilon-Greedy.
* **Regret Bounds:** Focuses heavily on bounding the regret of these algorithms, which measures how much worse the algorithm performs compared to always choosing the best arm.
* **Adaptive Exploration:** Introduces the idea of improving performance through adaptive exploration strategies (adjusting exploration based on observed rewards).
* **Clean Event:** Introduces the concept of the "clean event" to simplify analysis by focusing on high probability events.
* **Table of Contents:** Shows a detailed table of contents, indicating the breadth of topics covered in the full book including Bayesian Bandits, Contextual bandits, Adversarial bandits and connection with economics.
* **Multi-Armed Bandit Framework:** The document introduces the core concept of multi-armed bandits – a model for decision-making under uncertainty, often used as a simplified starting point for more complex reinforcement learning problems.
* **Applications:** It highlights several applications, including news website optimization, dynamic pricing, and medical trials.
* **Key Concepts:** Defines crucial concepts like arms, rewards, regret, exploration vs. exploitation, and different feedback mechanisms (bandit, full, partial).
* **Algorithms:** Presents and analyzes simple algorithms like Explore-First and Epsilon-Greedy.
* **Regret Bounds:** Focuses heavily on bounding the regret of these algorithms, which measures how much worse the algorithm performs compared to always choosing the best arm.
* **Adaptive Exploration:** Introduces the idea of improving performance through adaptive exploration strategies (adjusting exploration based on observed rewards).
* **Clean Event:** Introduces the concept of the "clean event" to simplify analysis by focusing on high probability events.
* **Table of Contents:** Shows a detailed table of contents, indicating the breadth of topics covered in the full book including Bayesian Bandits, Contextual bandits, Adversarial bandits and connection with economics.
Multi-armed bandits a simple but very powerful framework for algorithms that make decisions over time under uncertainty. This book provides a more introductory, textbook-like treatment of the subject, covering IID and adversarial rewards, contextual bandits, and connections to economics.
Details the restrictions when using a public MQTT broker with Meshtastic, focusing on TLS/SSL requirements, authentication, and potential issues with server reliability and rates.
