TMCnet

Data Normalization vs. Standardization: What's the Difference and When to Use Each

Data Normalization vs. Standardization is one of the most foundational yet often misunderstood topics in machine learning and ...
IEEE

Optimal Inverse Design Based on Memetic Algorithms—Part II: Examples and Properties

Abstract: Optimal inverse design, including topology optimization and evaluation of fundamental bounds on performance, which was introduced in Part 1, is applied to various antenna design problems. A ...
GitHub

TfELM : Extreme Learning Machines Framework with Python and TensorFlow

This framework provides a comprehensive set of tools and utilities for implementing and experimenting with Extreme Learning Machines using Python and TensorFlow. ELMs are a type of machine learning ...
How-To Geek on MSN

5 powerful Python one-liners that will make you a better coder

Why write ten lines of code when one will do? From magic variable swaps to high-speed data counting, these Python snippets ...
Communications of the ACM

Formal Reasoning Meets LLMs: Toward AI for Mathematics and Verification

A marriage of formal methods and LLMs seeks to harness the strengths of both.
GitHub

Distributed Rate Limiter

A production-ready distributed rate limiter supporting five algorithms (Token Bucket, Sliding Window, Fixed Window, Leaky Bucket, and Composite) with Redis backing for high-performance API protection.
IEEE

Verifying algorithms for autonomous aircraft by simulation - generalities and example

Abstract: An open question in Air Traffic Management is what procedures can be validated by simulation where the simulation shows that the probability of undesirable events is below the required level ...