This project implements a state-of-the-art CNN architecture for CIFAR-10 image classification, achieving 88.82% accuracy through systematic hyperparameter optimization. The implementation includes GPU ...

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Abstract: Hyperparameter tuning, such as learning rate decay and defining a stopping criterion, often relies on monitoring the validation loss. This paper presents NeVe, a dynamic training approach ...

Machine learning models are increasingly applied across scientific disciplines, yet their effectiveness often hinges on heuristic decisions such as data transformations, training strategies, and model ...

From electronic health records and blood tests to the stream of data from wearable devices, the amount of health information people generate is accelerating rapidly. Yet, many users struggle to ...

Official support for free-threaded Python, and free-threaded improvements Python’s free-threaded build promises true parallelism for threads in Python programs by removing the Global Interpreter Lock ...

Functions are the building blocks of Python programs. They let you write reusable code, reduce duplication, and make projects easier to maintain. In this guide, we’ll walk through all the ways you can ...

Functions are the building blocks of Python programming. They let you organize your code, reduce repetition, and make your programs more readable and reusable. Whether you’re writing small scripts or ...

BOSTON--(BUSINESS WIRE)--Method AI, a medical technology company focused on improving oncology outcomes through image-guided surgical navigation, announced today it has raised $20 million in Series A ...

Supervised Fine-Tuning (SFT) is a standard technique for adapting LLMs to new tasks by training them on expert demonstration datasets. It is valued for its simplicity and ability to develop ...

Abstract: For distributed-drive electric vehicles, torque vectoring control based on model predictive control (MPC) has emerged as a preferred strategy to achieve superior performance across diverse ...