Convolutional Neural Networks
(เครือข่ายประสาทเทียมคอนโวลูชั่น)
Definition
Convolutional Neural Networks (เครือข่ายประสาทเทียมคอนโวลูชั่น) Hard Skill
Convolutional Neural Networks (CNNs) are a class of deep learning models particularly effective for processing data with a grid-like topology, such as images. They utilize convolutional layers to automatically and adaptively learn spatial hierarchies of features, enabling pattern recognition and classification tasks.
Expertise Level
Level 1
Basic
1. Understands the basic structure and components of CNNs.
2. Can explain the purpose of convolution, pooling, and activation layers.
3. Able to implement simple CNN models using high-level libraries.
Level 2
Intermediate
1. Can design CNN architectures for different image processing tasks.
2. Understands hyperparameter tuning and optimization techniques for CNNs.
3. Able to preprocess and augment image datasets effectively for training.
Level 3
Advanced
1. Can develop custom CNN layers and novel architectures.
2. Able to implement advanced techniques like transfer learning and fine-tuning.
3. Can optimize CNNs for performance, including model compression and deployment in production.
