给作者发送电子邮件
You can use this page to email Afshine Amidi、Shervine Amidi、Tao(Thomas) Yu和Binbin Xiong about Super Study Guide: Transformer 与大语言模型.
关于本书
本书是一部讲解清晰、图解丰富的大语言模型技术指南，深度剖析核心原理与实战技巧，为大语言模型项目研发、面试或出于好奇心了解大语言模型核心原理的同学提供强大助力。
全书共分为五个部分：
- 基础篇： 以精炼严谨的内容帮助你迅速掌握神经网络基础与深度学习中的关键概念，为后续学习打下坚实理论基础
- 嵌入篇： 深入探讨分词算法、词嵌入（如 word2vec）以及句子嵌入（如 RNN、LSTM、GRU）
- Transformer篇： 详解自注意力机制背后的原理，全面解析编码器-解码器架构及其相关变种，如 BERT、GPT 和 T5，并附有加速计算的技巧与窍门
- 大语言模型篇： 探究基于 Transformer 模型的调优策略，包括提示工程、参数高效微调和偏好调整
- 应用篇： 聚焦情感抽取、机器翻译、检索增强生成等热门应用场景
此外，本页面提供自主领取 15% 的折扣优惠，旨在回馈已购买纸质版的读者，并兼顾各国家和地区的购买力平价。非常感谢您的关注与支持！
关于作者们
Afshine Amidi is currently teaching the Transformers & Large Language Models workshop at Stanford and is also leading LLM efforts at Netflix. Previously, he worked on the Gemini team at Google and used NLP techniques to solve complex queries. Before that, he worked at Uber Eats to improve the quality of the search and recommendation systems. On the side, Afshine published a few papers at the intersection of deep learning and computational biology. He holds a Bachelor’s and a Master’s Degree from École Centrale Paris and a Master’s Degree from MIT.
Shervine Amidi is currently teaching the Transformers & Large Language Models workshop at Stanford and is also working on the Gemini team at Google to leverage LLMs for action-based queries. Previously, he worked on applied machine learning problems for recommender systems at Uber Eats where he focused on representation learning to better surface dish recommendations. On the side, Shervine published a few papers at the intersection of deep learning and computational biology. He holds a Bachelor’s and a Master’s Degree from École Centrale Paris and a Master’s Degree from Stanford University.
Tao (Thomas) Yu earned his B.S. in Computer Science from Zhejiang University, and his M.S. in Computer Science from Carnegie Mellon University, and completed the Artificial Intelligence graduate program at Stanford University. Early in his career, he was a core member of Google Bard (now Gemini), leading research and engineering efforts across projects—from BERT and LaMDA to large-scale models powering personal assistants and AI agents. He later joined Meituan as Senior Technical Director of the AI Platform, where he also chaired the Technical Committee and led the Large Model Platform, architecting the company’s enterprise-level large language model ecosystem. Additionally, he co-founded 01.AI and spearheaded post-training and reinforcement learning for large language models. He was recognized in the Forbes China’s 2021 30 Under 30 Technology list.
Binbin Xiong is a Research Scientist in the GenAI unit at Google DeepMind, where he leads and co-leads multiple initiatives in Gemini pretraining data research. His work spans key areas such as data valuation, mixture design, and large-scale pretraining data scaling laws. Previously, Binbin spent several years specializing in applied machine learning and deep learning, with a focus on data-centric modeling and interpretability techniques to enhance Google’s products, including Search, YouTube, Waymo, Ads, and more. Outside of work, Binbin is passionate about bodybuilding and snowboarding. He is a NASM-certified personal trainer and an AASI-certified snowboard coach.