This repository contains beginner-level artificial intelligence and deep learning practice notebooks.
It covers perceptron-based logic gates, multilayer perceptron prediction, and convolutional neural network basics.
本專案整理人工智慧概論與深度學習基礎練習，內容包含感知器、MLP 多層感知器與 CNN 卷積神經網路等入門主題。
The purpose of this repository is to record and organize my introductory deep learning practice.
It focuses on understanding the basic structure and workflow of neural networks rather than building a production-level AI system.
| Chapter | Topic | Key Concepts |
|---|---|---|
| ch1-logic-gates-perceptron | Logic Gates with Perceptron | weights, bias, activation, AND / OR logic |
| ch2-mlp-prediction | MLP Prediction | hidden layers, model training, loss, prediction |
| ch3-cnn | CNN Basics | convolution, pooling, flatten, classification |
- Python
- Jupyter Notebook
- NumPy
- Deep learning libraries used in practice notebooks
- Basic neural network training workflow
| Course | Connection |
|---|---|
| Artificial Intelligence | Neural network concepts, model training, basic AI workflow |
| Statistics / Data Analysis | Data preprocessing, prediction, evaluation concepts |
| Programming Practice | Python-based implementation and experiment documentation |
Through this repository, I learned the basic concepts behind neural networks, including weights, bias, activation functions, hidden layers, loss, and training process.
I also practiced how CNNs use convolution and pooling to extract image features.
This repository is an introductory learning project.
It is intended to demonstrate AI learning progress and basic implementation practice, not advanced model research or production-level deep learning development.