#Natural language processing (NLP) full-stack practical tutorial

🎯 Tutorial Positioning: A high-quality Chinese NLP full-stack implementation guide, for developers who have a certain Python foundation and want to go from "only using large model APIs" to "understanding the underlying layer + being able to build small models + being able to do complex projects" 🔗 Pre-requisite skills package: Python 3.x proficient syntax, basic loop/branch/function, simple regular/list derivation, and knowing how to package with pip (linear algebra/probability only requires "human analogies" when understanding the principles, and will not block the way) ⏱ Expected learning cycle: 8-10 weeks, investing 10-15 hours per week (including 3 hours of hands-on coding) 📦 Supporting resources: All complete runnable codes, annotated data sets, and homework reference answers are updated simultaneously in the Daoman Python AI GitHub warehouse (jump links will be added to the corresponding chapters later)

#📚 2026 version of practical-oriented full-stack outline

We no longer follow the logic of traditional textbooks of "stack theory first", but start each chapter from "solving a small NLP problem". For example, in the first stage, we will take you to make a "Douban movie review keyword extractor" to practice TF-IDF, in the second stage, we will build a "simple translator based on GRU", and in the third stage, we will hand-write the "simplified version of Transformer core attention block" - ensuring that each step has visible code results.

#The first stage: text preprocessing (cornerstone · small demo pre-processing that can be implemented)

🎯 Core goal: Clean and break incomprehensible "human words" (unstructured text) into blocks and turn them into "numeric vector tables" that computers can calculate

#Phase 2: Deep Learning and Sequence Model (Advanced Chapter · Understand "Context Sequence")

🎯 Core goal: Solve the problem of "word meaning is fixed regardless of context" in the first stage of word vectors (for example, "apple" is always apple in the first stage, but "apples to eat" and "iPhones to use" can be distinguished in the second stage)

#The third stage: Transformer revolution (2026 AI core · If you master this, you will master the essence)

🎯 Core goal: Solve the problem of the second stage sequence model "can't remember the core information even if a sentence is too long" + "can only be processed serially (slow)" - this is also the common basis of all large models (GPT/BERT/LLaMA, etc.) now!

#The fourth stage: Pre-training model and transfer learning (Application · Standing on the shoulders of giants)

🎯 Core goal: No need to train Transformer from scratch! Directly call the "top pre-training models" on Hugging Face (such as Chinese BERT-base-chinese, LLaMA-2-7B, etc.), and you can solve your own problems with just a little "fine-tuning"!

#Stage 5: Ladder to Large Model (LLM)

🎯 Core goal: Understand the qualitative change process from "ordinary NLP model" to "large model (LLM)", learn to use large model API to do Prompt Engineering, and also learn to use parameter efficient fine-tuning (PEFT) to fine-tune large models with low cost!

#Phase Six: Industrial NLP Project Practice

🎯 Core goal: connect all the previous knowledge to solve complex problems in the real world! Each project will include the complete process of "demand analysis → data collection → data preprocessing → model selection → model training/fine-tuning → model evaluation → model deployment (simple version)".

#🗺️ Learning path map (pitfall avoidance version)

graph LR
    A[第一阶段：文本预处理<br>→ 豆瓣影评关键词提取器] --> B[第二阶段：序列模型<br>→ 基于GRU的简单中译英翻译器]
    B --> C[第三阶段：Transformer<br>→ 手写简化版Transformer编码器<br>（⚠️ 2026 AI 核心！必须掌握！）]
    C --> D[第四阶段：预训练模型<br>→ 基于BERT的电商差评分类器]
    D --> E[第五阶段：大模型<br>→ 基于LoRA的中文小说续写器]
    E --> F[第六阶段：工业级项目<br>→ 选1-2个感兴趣的做]

#🔧 2026 version recommended tool list (install on demand)

#📖 3 core features of the tutorial (different from other tutorials)

1. Avoid pitfalls first: "Guidelines for avoiding pitfalls in this chapter" will be added at the beginning of each chapter. For example, in the first stage, it will say "Don't use One-Hot for long text classification", and in the third stage, it will say "Don't write a complete Transformer from scratch, unless it is to learn the principles."
2. Humanized Principle: All complex principles are explained clearly using "analogies in life". For example, the gating mechanism of LSTM is compared to "notebook + sticky notes", and the attention mechanism is compared to "the keywords you stare at when the teacher asks questions."
3. Engineering Orientation: Each chapter has complete runnable code, and each project includes the complete process of "Requirements Analysis→Data Collection→Data Preprocessing→Model Selection→Model Training/Fine-Tuning→Model Evaluation→Model Deployment (Simple Version)", allowing you to find a job or work on your own project directly after completing the course.

🚀 Quick Start Lesson 1: 第一章 - NLP 2026：不只是聊天机器人！