Transformers: The Definitive Guide. Applications Beyond NLP Nicole Koenigstein

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Spis treści książki

• Preface
  • What This Book Is About
  • What This Book Is Not
  • Who This Book Is For
  • Navigating This Book
  • Conventions Used in This Book
  • Using Code Examples
  • OReilly Online Learning
  • How to Contact Us
  • Acknowledgments
• 1. From First Principles to State-of-the-Art Transformers
  • Transformer Basics
    • Tokenizer: Text Representation in the Transformer
    • Token and Positional Embeddings
    • Attention Mechanism
      • Multi-head attention
      • Bidirectional and unidirectional attention
    • Encoder and Decoder Parts
  • Enhancements in Transformer Design: Longer Context and Attention Variations
    • Longer Context Windows with Better Performance
    • Attention Mechanism Variations
      • Cross-attention
      • Multi-query attention
      • Grouped-query attention
      • FlashAttention
      • FlashAttention-2
      • FlashAttention-3
  • 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
      • Preparing your data for SAM
      • Setting up the model and logging with Weights & Biases
      • Preparing the hyperparameter tuning
      • Use the best hyperparameters to fine-tune SAM
  • 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: Whats That Noise About?
    • Classifier-Free Guidance in Diffusion Models
  • Scalable Diffusion Models with Transformers
    • Generating Images with the DiT
  • PIXART-
  • PixArt-
    • Generating Images with PixArt-
      • Memory-efficient image generation
      • Training a diffusion transformer
  • 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
      • Testing STORM with Road Runner
    • 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: Whats 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 Youre 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

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