C++ for Natural Language Processing
Build and Deploy NLP Systems with C++ and Modern AIBy Roland Kessler
Length7h 10m
About this audiobook
C++ natural language processing and NLP C++ programming. Unlock the raw power of C++ for natural language processing with this hands-on guide. Build fast text-processing tools—tokenizers, search engines, and classifiers—where C++ speed beats interpreted languages. From beginner to pro, master NLP with C++ and create high-performance applications.
In this practical book, Roland Kessler shows you how to leverage C++'s unmatched performance for natural language processing tasks. You'll start with tokenization and text normalization, then move to search algorithms and classification models. Each chapter includes complete code examples and real-world projects. Whether you're a data scientist, software engineer, or researcher, you'll learn to build tools that process text faster than Python or Java.
What you will learn:
Design custom tokenizers for any language
Implement efficient search algorithms with C++ STL
Build text classifiers using machine learning libraries
Optimize memory and CPU for large-scale NLP
Integrate with existing C++ projects
Unlike general C++ books, this focuses solely on NLP. It's the perfect companion for developers who want speed. Compare with [placeholder] and [placeholder] for a focused approach to text processing.
This hands-on C/C++/C#/.NET guide is written to be used at the keyboard: every concept is paired with something you can run, adapt, and keep. You move from first principles to real, working results, with the common errors and fixes called out along the way so you are never stuck for long.
Audiobook details
GenreTechnology
Length7 hrs 10 mins
Narrated byListen with 1,000+ voices
FormateBook with Audio
LanguageEnglish
Table of contents
1C++ for Natural Language Processing
2Preface
3Chapter 1 — Setting Up Your C++ NLP Development Environment
4Chapter 2 — Modern C++ Essentials for Text Processing
5Chapter 3 — Reading, Writing, and Normalizing Real-World Text
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6Chapter 4 — Tokenization: From Characters to Subwords
7Chapter 5 — N-gram Language Models and Text Generation
8Chapter 6 — Vector Space Models and Classic Search
9Chapter 7 — High-Performance Text Classification
10Chapter 8 — Sequence Labeling for Named Entity Recognition
11Chapter 9 — Static Word Embeddings and Analogies
12Chapter 10 — Neural Networks from Scratch in C++
13Chapter 11 — Sequence Modeling with LSTMs
14Chapter 12 — Transformer Inference with ggml and llama.cpp
15Chapter 13 — Building a Complete RAG System
16Chapter 14 — Production: Profiling, Serialization, and Packaging
17Chapter 15 — Capstone: Train Your Own Mini-GPT from Scratch
18Chapter 16 — Advanced Decoding and Sampling Strategies
19Chapter 17 — Putting It All Together: The Unified Assistant
20Chapter 18 — Where to Go Next: The C++ NLP Frontier
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