- Transformer权威指南(影印版)(英文版)
- - 作者：(美)尼科尔·柯尼希施泰因|责编:张烨
  - 出版社：东南大学
  - ISBN：9787576629521
  - 出版日期：2026/07/01
  - 页数：353
- 售价：63.2

内容大纲
    在音频、视频以及复杂数据分析等领域，AI技术的巨大潜力尚未被充分挖掘。事实上，由于缺乏指导和实际应用案例，很多当今的专业人士在将AI创新成果应用于这些多元领域时仍面临挑战。
    这本全面指南填补了这一空白，专为中高级机器学习工程师、数据科学家和研究人员量身打造。作者Nicole Koenigstein带领读者深入探索Transformer模型的多样化应用，不仅深化理论理解，更着重强调了面向实际应用的可操作策略。本书将提供关于Transformer的“大统一理论”——这些基础洞见将确保你始终立于技术前沿，无论最先进的模型如何演变。
    通过本书，你将学会将Transformer应用于：
    图像、视频、音乐生成等非文本领域。
    推理模型、代码智能体以及多智能体架构。
    训练阶段与推理阶段的优化策略。
    生产部署、运行时工程以及硬件效率优化。
作者介绍
尼科尔·柯尼希施泰因是智能体系统领域的AI研究员与实践者，活跃于科研、咨询、教学以及系统落地等多个领域，致力于构建可靠、可用于生产环境的AI系统。她著有AI Agents：The Definitive Guide（O'Reilly 2026年出版）等多部著作，曾担任欧盟委员会AI大挑战（European Commission AI Grand Challenge）项目的外部评审专家。
目录
Preface
1.From First Principles to State-of-the-Art Transformers
  Transformer Basics
    Tokenizer: Text Representation in the Transformer
    Token and Positional Embeddings
    Attention Mechanism
    Encoder and Decoder Parts
  Enhancements in Transformer Design:
    Longer Context and Attention Variations
    Longer Context Windows with Better Performance
    Attention Mechanism Variations
  Conclusion
2.Transformers for Time Series
  Understanding the Intricacies of Time Series Data
    Autocorrelation and Partial Autocorrelation
    Cointegration
    Cross-Correlation
    Stationarity
    Trend and Seasonality
  Preparing a Dataset
  Time Series Modeling in Various Application Domains
    Tokenizing Time Series Data
    Chronos: Learning the Language of Time Series
    Fine-Tuning Chronos
    PatchTST: A Time Series Is Worth 64 Words
    Fine-Tuning PatchTST on Historical IBM Stock Prices
    TimesFM: A Decoder-Only Time Series Foundation Model
    Fine-Tuning TimesFM on Hourly Energy Consumption Data
    AnomalyBERT for Self-Supervised Anomaly Detection
  Conclusion
3.Transformers for Vision Tasks
  Overview of Different Vision Tasks
  Embeddings and Tokenization for Vision Models
  Key Strategies for Improving the Robustness and Effectiveness of Vision Tasks
  Swin Transformer V2
    Image classification with Swin Transformer V2
  Segment Anything
    Fine-Tuning SAM on a Custom Dataset
  Segment Anything in Images and Videos
  Segment Videos and Images with Concept Prompts
  Conclusion
4.Transformers for Image Generation
  Introduction to Generative Image Models
  Diffusion Models: What's That Noise About?
    Classifier-Free Guidance in Diffusion Models
  Scalable Diffusion Models with Transformers
    Generating Images with the DiT
  PIXART-α
    Generating Images with PixArt-Σ
  Diffusion Vision Transformers for Image Generation

  Interpretable Features with Diffusion Transformers
  Conclusion
5.Transformers for Video Generation
  Hidden Effectiveness of Latent Diffusion
  LTX-Video: Video in Realtime
  Latte: Structured Detail, Poured into Every Video Frame
  Tora: From Trajectory to Storyline, One Frame at a Time
  Conclusion
6.From Sound to Token and Back: Transformers in the Audio Domain
  From Waveforms to Spectrograms:
    Understanding the Structure of Audio Data
    Audio as a Waveform
    Sampling Rate and the Nyquist Theorem
    Amplitude, Bit Depth, and Quantization
    The Frequency Domain and Fourier Transform
    Spectrograms and the Short-Time Fourier Transform
    The Mel Spectrogram and Perceptual Scaling
    Phase, Reconstruction, and Vocoders
  Audio Modeling in Various Application Domains
  Transformer Architectures for Audio:
    From Perception to Foundational Intelligence
    The Rise of Speech Transformers: The Impact of Whisper
    Audio Foundation Models: Unifying Understanding, Generation, and Conversation
    Qwen2-Audio
    Transcribing a Meeting with Kimi-Audio
    Segment Anything in Audio
    Beyond Text and Speech: Transformers as Music Composers
  Conclusion
7.Reinforcement Learning Transformers
  Getting Started with Reinforcement Learning
  Foundational Concepts in Reinforcement Learning
    Online and Offline Reinforcement Learning
    Model-Based and Model-Free Approaches
    On-Policy Versus Off-Policy Reinforcement Learning
    Temporal Difference Learning
    World Models in Reinforcement Learning
  Transformers in Reinforcement Learning
    Decision Transformer
    Going Live: Online Decision Transformer
    A Brave New World: Stochastic Transformer-Based World Model
    TWISTER: Transformer-Based World Models with Contrastive Predictive Coding
  Conclusion
8.Embracing the Era of Experience: Transformers for Planning, Reasoning, and Coding
  From Human Data to Lived Experience
  Learning to Reason: From Pretraining to Reinforcement Learning
  DeepSeek-R1: Reinforcing Reasoning Capabilities
  Qwen3: Unified Reasoning with Dynamic Control
  Qwen3-Coder: Agentic Reasoning for Open-Ended Coding
  Kimi K2: Open Agentic Intelligence at Scale
    Muon: Scaling Optimization for the Agentic Era

    Inference with Kimi K2
  Scaling Reasoning at Test-Time: Smarter, Not Just Bigger
    Adaptive Branching Monte Carlo Tree Search (AB-MCTS)
    The RethinkMCTS Framework for Code Generation
    The S* Framework for Code Generation
  Conclusion
9.From Scripts to Thinking: AI Agents for Complex Tasks
  Autonomy: What's Possible at the Moment?
  Designing Agent Workflows
    Multi-Agent Architectures
  Agentic Communication: The Right Context Is All You Need
  Beyond Context: How to Help Agents Remember
    Agent Memory Types
    Going Global and Lifelong
  The Human Factor: Steering Agent Actions
    Common Patterns for Human-in-the-Loop
    Solving GitHub Issues with Coding Agents
  Conclusion
10.Smarter, Better, Faster, Stronger: Optimizing LLMs and AI Agents
  Training-Time Intelligence: Reinforcement Learning for Agents
    Beyond Hand-Crafted Rewards: How RULER Works
    Training in Practice: ART in a Market Scenario
  Reason Smarter, Not Harder: Adaptive Compute Allocation
    The Delta Incentive: Enforcing Efficiency
  Open Innovation: Community-Driven RL Frameworks
  The Checkpoint Engine: Systems-Level Optimization for LLM Policy Updates
  Conclusion
11.Deploying Transformer Models
  Choosing Between Open and Closed Source
  Understanding the Architecture You're Deploying
  Deploying Decoder-Only Models
    Runtime Engineering for Decoder-Only Models
    Security Considerations for Decoder-Only Deployments
  Building Applications with Coding Models
  Evaluating LLM Deployments in Production
    Cost Efficiency and Hardware Comparison
  Quantization
  Test-Time Low-Rank Adaptation in Vision-Language Models
  Conclusion
12.Where to Go Next: From Models to Intelligent Systems
  Combining Capabilities: SAM 3 Agent
  The Science of Scaling Agentic Systems
  Conclusion
Index

内容大纲

作者介绍

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