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    3. Generative AI in C++: Coding Transformers and LLMs

    Generative AI in C++: Coding Transformers and LLMs

    Posted By: TiranaDok
    Date: 21 Apr 2025 16:18:54
    Generative AI in C++: Coding Transformers and LLMs

    Generative AI in C++: Coding Transformers and LLMs (Generative AI LLM Programming) by David Spuler, Michael Sharpe, Cameron Gregory
    English | March 30, 2024 | ISBN: N/A | ASIN: B0CXJKCWX9 | 1206 pages | EPUB | 1.15 Mb

    Do you know C++ but not AI? Do you dream of writing your own Generative AI engine in C++? From beginner to advanced, this book covers the internals of GPT-style Transformer engines and Large Language Models (LLMs) in C++, with source code examples and research paper citations.Key Features
    • Transformer components in C++
    • Faster and smarter AI
    • Open source LLMs
    • Advanced software development
    • Cutting-edge research optimizations
    • Just C++ code without all the math
    • Research papers literature survey
    Table of Contents

    Part I: AI Projects in C++
    1. Introduction to AI in C++
    2. Transformers & LLMs
    3. AI Phones
    4. AI on Your Desktop
    5. Design Choices & Architectures
    6. Training, Fine-Tuning & RAG
    7. Deployment Architecture

    Part II: Basic C++ Optimizations
    8. Bitwise Operations
    9. Floating Point Arithmetic
    10. Arithmetic Optimizations
    11. Compile-Time Optimizations
    12. Pointer Arithmetic
    13. Algorithm Speedups
    14. Memory Optimizations

    Part III: Parallel C++ Optimizations
    15. Loop Vectorization
    16. Hardware Acceleration
    17. AVX Intrinsics
    18. Parallel Data Structures

    Part IV: Transformer Components in C++
    19. Encoders & Decoders
    20. Attention
    21. Activation Functions
    22. Vector Algorithms
    23. Tensors
    24. Normalization
    25. Softmax
    26. Decoding Algorithms
    27. Tokenizer and Vocabulary

    Part V: Optimizing Transformers in C++
    28. Deslugging AI Engines
    29. Caching Optimizations
    30. Vectorization
    31. Kernel Fusion
    32. Quantization
    33. Pruning
    34. MatMul/GEMM
    35. Lookup Tables & Precomputation
    36. AI Memory Optimizations

    Part VI: Enterprise AI in C++
    37. Tuning, Profiling & Benchmarking
    38. Platform Portability
    39. Quality
    40. Reliability
    41. Self-Testing Code
    42. Debugging

    Part VII: Research on AI Optimization
    43. Overview of AI Research
    44. Advanced Quantization
    45. Knowledge Distillation
    46. Structured Pruning
    47. Early Exit and Layer Pruning
    48. Width Pruning
    49. Length Pruning
    50. Adaptive Inference
    51. Zero-Multiplication Models
    52. Logarithmic Models
    53. Arithmetic Optimization Research
    54. Ensemble Multi-Model Architectures
    55. Advanced Number Systems
    56. Neural Architecture Search
    Appendix 1: C++ Slug Catalog

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