This book is edited by Li Hui and Chen Yanyan, with associate editors Yang Yu, Gao Yong, Zhang Qiaosheng, Bi Ye, and Liu Dengzhi. It is rich in content, covering 32 theories and 32 practical cases, ...

Find out how today's engineers succeed by growing their technical abilities, improving how they communicate, and staying open ...

Learn how to fine-tune GPT-OSS efficiently with LoRa and quantization. A beginner-friendly guide to optimizing AI models on ...

Quantum machine learning takes classical data and encodes it in quantum states. The quantum computer can then uncover patterns in the data that would be hard for classical systems to detect.

We define machine learning and explain how it works within machine vision, with a focus on where machine learning can be effectively applied to enhance inspection reliability and capability.

In this paper, Austin Whisnant describes a machine learning model used to build a corpus of insider threat data to support insider threat research.

The challenge is developing practical, hands-on tools that enable researchers, educators, and students to effectively apply deep learning techniques to protein design tasks, bridging theoretical ...

Personal Large Language Models (LLMs) projects, particularly related to open-source models. These projects encompass frameworks for LLM training, deployment tools, tutorials, and practical ...