Pupil Learning Mechanism

In our research, we followed a pupil learning procedure involving interpreting, picking, understanding, cramming, and organizing. I implemented the PLM model in Python and evaluated it using a real-world copper price dataset. This innovative approach addresses the challenges of vanishing gradients and overfitting in neural networks. By implementing PLM, we significantly improved the performance and adaptability of neural networks during training. Our empirical results demonstrated the effectiveness of PLM, showing superior performance compared to linear regression and conventional backpropagation-based neural networks.

Key Achievements:

Sequential Learning: The model learns data in a sequence, handling new instances by understanding them through existing knowledge and cramming when necessary.

Adaptive Learning: The network adapts its structure dynamically to improve learning outcomes.

Perfect Learning: Ensures minimal error in learning instances.

Less-Overfitted Learning: Reduces overfitting by pruning irrelevant nodes and regularizing weights.

Empirical Validation:

We conducted extensive experiments on a copper price forecasting dataset. The PLM model consistently outperformed traditional models in terms of accuracy and generalization, handling both the vanishing gradient problem and overfitting effectively.

Learning Outcomes:

Through this project, I enhanced my research skills, critical thinking, and problem-solving abilities. The experience solidified my interest in AI and computer science, motivating me to pursue a master's degree to explore these areas further.

• Python
• PyTorch
• Deep Learning
• Machine Learning
• Neural Network Optimization
• Pupil Learning Mechanism
• Vanishing Gradients
• Overfitting Solutions
• Adaptive Learning