From Perceptrons to Transformers: The Evolution of Neural Networks

This open-source GitHub project maintained by rnilav provides a complete learning path from neural network basics (perceptrons) to core technologies of modern large language models (Transformers), suitable for learners who want to deeply understand the principles of deep learning. The project explains concepts progressively along the脉络 of technological development, filling the knowledge gap between LLM applications and underlying principles.

Original Author & Source

• Original Author/Maintainer: rnilav
• Source Platform: GitHub
• Original Title: perceptrons-to-transformers
• Original Link: https://github.com/rnilav/perceptrons-to-transformers
• Release Date: June 16, 2026

Project Positioning

Against the backdrop of LLM becoming a热门 technology, many learners lack an understanding of underlying principles. This project aims to provide a systematic, progressive learning path that guides learners along the historical脉络 of neural network development to understand the background of key technologies and the problems they solve.

Perceptron: The Starting Point of Neural Networks

• Proposer: Frank Rosenblatt (1957)
• Core Concept: A binary linear model that learns input-output mapping through weight adjustment
• Historical Significance: Triggered the first AI wave, but single-layer perceptrons cannot solve the XOR problem, leading to the first neural network winter

Multilayer Perceptron (MLP) and Backpropagation

• Multi-layer Structure: Introduces hidden layers to gain non-linear modeling capabilities; the Universal Approximation Theorem proves it can approximate any continuous function
• Backpropagation: Proposed by Rumelhart et al. in 1986, uses the chain rule to calculate gradients and is the cornerstone of deep learning training

Convolutional Neural Network (CNN)

• Convolution Operation: Local connection + weight sharing reduces parameters, preserves spatial structure, inspired by biological vision
• Milestone Models: LeNet→AlexNet→VGG→ResNet (residual connections solve gradient vanishing)

Recurrent Neural Network (RNN)

• Temporal Dependency: Uses cyclic connections to memorize previous information and handle variable-length sequences
• Variants: LSTM/GRU introduce gating mechanisms to solve long-term dependency problems

Transformer Core

• Attention Mechanism: Self-attention allows direct connections between sequence positions and dynamically integrates context
• Parallelization Advantage: Abandons cyclic structure, can compute the entire sequence in parallel, improving training efficiency
• Key Components: Multi-head attention, positional encoding, layer normalization, feed-forward network

This architecture gave birth to large language models like BERT and GPT

Basic Stage

Start with perceptrons and MLP, understand forward propagation, backpropagation, and gradient descent, and implement simple networks hands-on

Advanced Stage

Learn CNN/RNN while practicing with application scenarios (image classification, text generation) to understand the problems each architecture is suitable for

Modern Stage

First grasp the intuitive meaning of the attention mechanism, then dive into its mathematical implementation, and read the original paper Attention Is All You Need

From perceptrons to Transformers, neural networks have evolved over nearly 70 years. This project provides a clear path for learners to understand this history.

Understanding underlying technologies not only has academic value but is also crucial for solving issues like LLM hallucinations and biases. AI technology develops rapidly, but basic knowledge such as linear algebra, calculus, and optimization theory remains the foundation of innovation.