Building Large Language Models from Scratch: A Systematic Bottom-Up Learning Path

This article introduces the ai-learning project, which helps learners gain an in-depth understanding of the working principles of large language models (LLMs) by building all components of an LLM from scratch. Addressing the limitations of existing resources, the project adopts a bottom-up, progressive approach, allowing learners to gradually master everything from basic tools to complete architectures, transitioning from 'knowing what' to 'knowing why'.

LLMs have become a technical hot topic, but they remain a 'black box' for most people. Existing resources fall into two extremes: either high-level overviews lack implementation details, or they directly call ready-made frameworks/pre-trained models, making it difficult for learners to grasp the underlying principles and limiting the in-depth development of the AI field.

The project adopts a bottom-up, from-scratch construction method. Its core is to understand LLM principles by hands-on implementation of each component, drawing on classic concepts in computer science education (such as learning operating systems by writing a simple kernel). It uses a progressive design, gradually building complex systems from simple components, lowering the barrier to entry and clearly showing the role and collaboration of each component.

The learning path is divided into five stages:

1. Basic Mathematics and Tools: Master the application of linear algebra/probability theory in deep learning, and implement tensor operations, matrix multiplication, and automatic differentiation;
2. Neural Network Basics: Build forward/backward propagation, activation functions/loss functions, and implement a simple multi-layer perceptron;
3. Sequence Models and Attention: Implement RNN/LSTM, and understand dot-product attention, multi-head attention, and positional encoding;
4. Transformer Architecture: Assemble encoder-decoder, layer normalization, residual connections, and complete the full model;
5. Training and Optimization: Learn data preprocessing, batch training, learning rate scheduling, and understand pre-training/fine-tuning and distributed training.

The practical value is reflected in three aspects:

1. In-Depth Understanding: Master the internal mechanisms of the model, quickly diagnose problems, and guide architecture design;
2. Engineering Capabilities: Cultivate skills such as project organization, debugging and training, and performance evaluation;
3. Research Foundation: Provide a solid foundation for AI research, cultivate 'first principles' thinking, and support original solutions.

Learning recommendations:

1. Active Practice: Learn by doing, do not skip implementation steps; try to solve problems independently first before referring to solutions;
2. Recording and Reflection: Maintain notes to record ideas, problems, and solutions, and review them regularly;
3. Community Communication: Participate in discussions and sharing, use community resources to solve difficulties and expand horizons.

The ai-learning project enables in-depth understanding of LLMs through hands-on construction, which is an important investment in AI learning. After completion, you can explore directions such as advanced architectures (sparse attention, state space models), multimodal learning, model compression/efficient inference, alignment and safety, where the project's foundation will play a key role.