Neural Networks
(โครงข่ายประสาทเทียม)
Definition
Neural Networks (โครงข่ายประสาทเทียม) Hard Skill
Neural Networks are computing systems inspired by the biological neural networks of animal brains, designed to recognize patterns and interpret complex data through interconnected layers of nodes.
Expertise Level
Level 1
Basic
1. Understand the fundamental concepts of artificial neurons and layers.
2. Can explain how a simple neural network processes input and generates output.
3. Familiar with common activation functions like sigmoid and ReLU.
Level 2
Intermediate
1. Can design and implement neural networks for basic classification or regression tasks.
2. Understand training processes including forward and backpropagation.
3. Able to tune hyperparameters like learning rate, epochs, and batch size for improved performance.
Level 3
Advanced
1. Design and optimize deep neural networks with multiple layers and architectures.
2. Apply advanced techniques such as regularization, dropout, and batch normalization.
3. Analyze and interpret model performance, troubleshoot issues like overfitting or vanishing gradients.
