DeepSeek Coder Training in Kuala Lumpur, Malaysia

This course is designed to provide developers and technical professionals with practical knowledge and hands-on experience using DeepSeek Coder, a powerful code generation large language model. Over two days, participants will learn how to prompt, integrate, and evaluate DeepSeek Coder for real-world coding tasks. The course includes foundational concepts, prompt engineering techniques, integration strategies, and best practices.

Introduction and Foundations

Module 1: Introduction to DeepSeek Coder
Understand what DeepSeek Coder is
Learn about the architecture and model type
Compare with other Code LLMs like Codex, CodeLlama, and StarCoder
Identify key use cases: code generation, explanation, translation, debugging

Module 2: Setup and Access
How to access DeepSeek Coder via HuggingFace, API, or local instance
Install necessary Python packages
Run your first code completion example

Module 3: Prompt Engineering for Coding Tasks
Learn the structure of a good code prompt
Write prompts for generating functions, explaining code, and translating languages
Test variations in prompting and analyze results

Module 4: Performing Specific Tasks
Use prompts for automatic documentation
Use prompts for bug detection and resolution
Use prompts to refactor messy or inefficient code

Advanced Usage and Customization

Module 5: Real-World Use Cases
Explore how to integrate DeepSeek Coder in real development environments
Understand automation in CI/CD workflows
Explore use in VSCode or notebooks for live assistance

Module 6: Advanced Prompting
Apply few-shot prompting to demonstrate complex code tasks
Use Chain of Thought prompting to improve logical reasoning in generated code
Simulate peer review or pull request feedback via prompts

Module 7: Evaluation and Limitations
Learn about ways to evaluate model output
Discuss strengths and weaknesses of DeepSeek Coder
Explore ethical considerations in LLM-based coding

Module 8: Extensions and Future Use
Compare with other emerging models
Explore ideas for customization or fine-tuning
Discuss multi-agent use cases for collaborative AI coding

Final Lab and Evaluation

ProgNXT Certification