NLPBook: An Open-Source Chinese Textbook on Neural Networks and Large Language Models by Northeastern University's NiuTrans Team

NLPBook is a comprehensive open-source textbook launched by the NiuTrans Lab at Northeastern University. It systematically explains neural network and natural language processing (NLP) technologies, covering a complete knowledge system from basic theories to large language models, providing Chinese readers with high-quality learning resources for deep learning in NLP. This textbook was released on GitHub (link: https://github.com/NiuTrans/NLPBook) on May 29, 2026.

Project Background

NLPBook is an open-source textbook project launched by the NiuTrans Lab. It aims to provide Chinese readers with systematic and comprehensive learning materials on neural networks and natural language processing, reflecting the team's academic vision of promoting technology popularization and cultivating NLP talents.

Team Introduction

The NiuTrans Lab is affiliated with the School of Computer Science and Engineering at Northeastern University. It is an important research institution in the field of natural language processing in China, long committed to research in machine translation, text understanding and generation, etc. It is one of the earliest teams in China engaged in machine translation research and has published a large number of high-level academic papers.

Basic Theory Section

• Neural Network Basics: Perceptron, Multi-Layer Perceptron (MLP), Activation Functions, Backpropagation
• Word Embeddings and Distributed Representations: Classic methods like Word2Vec, GloVe
• Sequence Modeling: RNN, LSTM, GRU and their applications
• Attention Mechanism: Principles and Implementation

Advanced Technology Section

• Transformer Architecture: Self-Attention, Multi-Head Attention, Positional Encoding
• Pre-trained Language Models: BERT, GPT, RoBERTa, ALBERT
• Generative Models: Autoregressive Modeling, Decoding Strategies, Text Generation Evaluation
• Multilingual and Cross-Lingual: Multilingual Pre-training, Cross-Lingual Transfer Learning

Large Language Model Special Topic

• Scaling Laws and Emergent Abilities
• Training Optimization: Distributed Training, Mixed Precision Training, Gradient Checkpointing
• Instruction Fine-tuning and Alignment: SFT, RLHF
• Model Evaluation and Capability Analysis

Integration of Theory and Practice

Provides abundant code examples and practical projects, allowing learners to practice while learning to develop the ability to solve real-world problems.

Chinese Context Optimization

Terminology translation is accurate and standardized; cases are close to the Chinese context, avoiding comprehension barriers from translated English textbooks.

Open-Source Collaboration Model

• Continuous Updates: Iterate based on technological developments and reader feedback
• Community Contributions: Readers can participate in error correction, supplement cases, and contribute code
• Free Access: Lower the threshold for learning
• Transparent and Open: Content is publicly reviewable

• Beginners: Provides a systematic learning path from scratch; it is recommended to read chapter by chapter with practice.
• Practitioners for Advancement: The large language model special topic and engineering practice chapters help quickly keep up with cutting-edge technologies.
• Researchers for Reference: Can be used as a field overview and technical reference, especially in multilingual NLP and machine translation directions.
• Teaching Use: University teachers can use it as a textbook or reference book; the open-source feature allows flexible content adjustment.

Zero-Basis Entry Path

1. Mathematical Basics: Linear Algebra, Probability and Statistics, Calculus
2. Python Programming: Basics and NumPy, PyTorch libraries
3. Basic Section Learning: Neural Network Basics, Word Embeddings
4. Practical Projects: Complete supporting basic programming exercises
5. Advanced Learning: Transformer and Pre-trained Models
6. Large Model Special Topic: Principles and Applications

Accelerated Path for Readers with Basic Knowledge

1. Fill in Gaps: Quickly browse basic chapters and mark unfamiliar content
2. Focus on Breakthroughs: Transformer Architecture and Attention Mechanism
3. Follow Cutting-Edge: Large Language Model Chapters
4. Project Practice: Choose an interested application direction for practice

NLPBook fills the gap in high-quality Chinese educational resources for deep learning in NLP and is a rare systematic textbook in the Chinese community. The open-source model gives it the ability to evolve continuously, and it is expected to become an important infrastructure for cultivating Chinese NLP talents. For readers interested in the NLP field, it provides a solid starting point; with practice, they can build a complete knowledge system and lay the foundation for deepening their work in this field.