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Course Outline
Introduction to DeepSeek LLM Adaptation
- Summary of DeepSeek model families, such as DeepSeek-R1 and DeepSeek-V3
- Rationale for adapting large language models
- Adapting vs. prompt engineering: A comparative analysis
Dataset Preparation for Adaptation
- Selecting domain-specific data collections
- Techniques for data preprocessing and cleansing
- Tokenization and formatting datasets for DeepSeek LLM compatibility
Establishing the Adaptation Environment
- Configuring GPU and TPU hardware acceleration
- Integrating Hugging Face Transformers with DeepSeek LLM
- Managing hyperparameters for effective adaptation
Executing DeepSeek LLM Adaptation
- Implementing supervised learning approaches
- Leveraging LoRA (Low-Rank Adaptation) and PEFT (Parameter-Efficient Fine-Tuning)
- Conducting distributed adaptation for extensive datasets
Assessment and Optimization of Adapted Models
- Measuring model performance using key metrics
- Addressing challenges related to overfitting and underfitting
- Improving inference latency and overall model efficiency
Deployment of Adapted DeepSeek Models
- Packaging models for API integration
- Embedding adapted models into software applications
- Scaling deployments via cloud infrastructure and edge computing
Practical Use Cases and Applications
- Applying adapted LLMs in finance, healthcare, and customer service sectors
- Industry-specific case studies
- Ethical implications of domain-specific AI systems
Course Summary and Future Directions
Requirements
- Practical experience with machine learning and deep learning software frameworks
- Knowledge of transformer architectures and large language models (LLMs)
- Competence in data preprocessing and model training methodologies
Target Audience
- AI scientists investigating LLM adaptation techniques
- Machine learning engineers engineering bespoke AI architectures
- Skilled developers deploying AI-powered solutions
21 Hours
