Zero-Basis AI Hands-On (Deep Learning + PyTorch): Essential AI Foundations

Course catalog:

1. Course Introduction
– Understand the course content and learning objectives.

2. Neural Network Task Analysis
– Analyze the tasks neural networks need to accomplish, laying the foundation for learning.

3. Model Update Methods Explained
– Interpret methods and principles of model updating, mastering model optimization techniques.

4. Loss Function Calculation Methods
– Learn loss function calculation and understand the standards for evaluating model performance.

5. Forward Propagation Workflow Explained
– Deeply analyze the flow and principles of neural network forward propagation.

6. Backpropagation Demonstration
– Live demonstration of backpropagation to deepen understanding and mastery.

7. Neural Network Architecture Breakdown
– Detailed analysis of overall neural network architecture, clarifying the function of each part.

8. Effect Visualization Analysis
– Use visualization tools to analyze neural network effects and optimize model performance.

9. The Role of Neuron Counts
– Explore how neuron counts affect model performance and optimize network structure.

10. Preprocessing & Dropout
– Learn data preprocessing and dropout techniques to improve model generalization.

11-25. Convolutional Neural Networks & Applications
– Step-by-step analysis of CNN principles and applications for image processing tasks.

26-32. PyTorch Framework & Data Processing
– Deep dive into the PyTorch framework and data processing methods to build and train models.

33-45. Deep Learning Model Training
– Learn deep learning model training workflows and techniques to improve results.

46-60. Text Data Processing & Training
– Explore text data processing and training techniques for NLP tasks.

61-69. Model Deployment & Serving
– Learn model deployment and server-side processing to apply models in real scenarios.

70-76. Vision Transformer Models
– Analyze the principles and implementation of Vision Transformer models for image processing.

77. Project Source Code Debugging & Practice
– Demonstrate the debugging process of project source code to help students understand and apply what they've learned.