Convolutional Neural Networks (CNN)
(โครงข่ายประสาทเทียมแบบคอนโวลูชัน (CNN))
Definition
Convolutional Neural Networks (CNN) (โครงข่ายประสาทเทียมแบบคอนโวลูชัน (CNN)) Hard Skill
Convolutional Neural Networks (CNN) are a class of deep learning models designed to process structured grid data such as images, using convolutional layers to automatically and adaptively learn spatial hierarchies of features.
Expertise Level
Level 1
Basic
1. Understands the basic structure and purpose of CNNs.
2. Can explain key components such as convolutional layers, pooling, and activation functions.
3. Familiar with simple CNN architectures for image recognition.
Level 2
Intermediate
1. Can design and implement CNN models using popular deep learning frameworks.
2. Understands how to tune hyperparameters such as filter size, stride, and padding.
3. Able to apply CNNs to solve real-world tasks like object detection or classification.
Level 3
Advanced
1. Masters advanced CNN architectures including ResNet, Inception, and DenseNet.
2. Can optimize CNNs for performance, accuracy, and computational efficiency.
3. Able to innovate and customize CNN models for specialized applications and research.
