Deep Neural Networks (DNN)
(เครือข่ายประสาทเทียมเชิงลึก)
Definition
Deep Neural Networks (DNN) (เครือข่ายประสาทเทียมเชิงลึก) Hard Skill
Deep Neural Networks (DNN) are advanced machine learning models composed of multiple layers of interconnected neurons that can learn hierarchical representations of data for complex pattern recognition and decision-making tasks.
Expertise Level
Level 1
Basic
1. Understands the basic structure of neural networks and key components such as neurons, layers, and activation functions.
2. Can explain the concept of deep learning and difference from traditional machine learning.
3. Familiar with simple feedforward networks and common terminologies.
Level 2
Intermediate
1. Can design and implement basic DNN architectures using popular frameworks (e.g., TensorFlow, PyTorch).
2. Understands backpropagation, gradient descent, and optimization techniques.
3. Able to preprocess data and tune hyperparameters to improve model performance.
Level 3
Advanced
1. Can build and optimize complex DNN architectures including convolutional, recurrent, and transformer-based networks.
2. Experienced in addressing overfitting, underfitting, and using techniques like dropout, batch normalization, and advanced optimizers.
3. Able to interpret model behavior and integrate DNNs into large-scale applications for real-world problem solving.
