AI model optimization and performance improvement strategies and latest research trends master curriculum

lecture background

There are problems often encountered when applying AI models to actual services.

If the model is too big or too slow to be practical
When it is difficult to apply the techniques of the latest paper to an actual project
When hyperparameter tuning is difficult to proceed systematically
When AI needs to be leveraged in environments with limited GPU resources

To solve these problems, model optimization is essential.

Course features

Practice-oriented configuration It focuses on practical implementation and application rather than theoretical explanation. Ready-to-use code and exercises are provided for each topic.

Reflects the latest research We will implement the core content of the latest 2024-2025 papers (LoRa, QLoRa, Zero-Shot, Few-Shot, etc.) in actual code.

Step by step learning It is designed so that you can learn everything from basic mathematics to advanced optimization techniques in a systematic manner.

Key learning content

Theoretical basics Summarize essential concepts such as optimization mathematics, loss functions, and performance indicators

data optimization

Improving data level performance through preprocessing, augmentation, and sampling techniques

Hyperparameter tuning Systematic tuning methods from grid search to Bayesian optimization

Model weight reduction Optimizing model size and speed through pruning and quantification

Transfer learning How to make effective use of pre-training models

Ensemble techniques Improve performance through bagging, boosting, and stacking

Latest research trends How to review articles and use open source

Practical projects Comprehensive optimization exercises using the TTS model

Course target

Those who have experience using Python/PyTorch and know basic deep learning concepts
Researchers who want to apply the latest AI research trends to practice
Practitioners who need to utilize AI in an environment with limited resources

Expected effects

Practical optimization experience, such as improved inference speed and memory savings
Ability to apply the latest AI papers to real projects
Effective use of AI in resource-constrained environments