Overview

This course provides an in-depth introduction to DeepSeek, an open-source large language model, focusing on its core architecture, reasoning processes, and optimal prompting strategies. Learners will gain a comprehensive understanding of DeepSeek’s groundbreaking technical innovations, such as Mixture of Experts and memory-efficient attention mechanisms, and explore its advanced reasoning and interpretability aspects. By the end of this course, learners will develop practical skills in structuring effective prompts, optimizing interactions with DeepSeek, and troubleshooting common challenges. The course provides a balanced mix of theory and hands-on practice, ensuring learners can apply what they’ve learned to real-world scenarios. What sets this course apart is its emphasis on bridging the gap between theory and application. Learners will not only learn how DeepSeek works but will also acquire actionable techniques for leveraging its full potential. This course is designed for those interested in understanding and working with DeepSeek, particularly AI researchers, developers, and data scientists. A basic understanding of machine learning and large language models is recommended. This course is part one of a three-course Specialization designed to provide a comprehensive learning pathway in this subject area. While it delivers standalone value and practical skills, learners seeking a more integrated and in-depth progression may benefit from completing the full Specialization.

Syllabus

What is DeepSeek?

This module introduces DeepSeek, an advanced open-source large language model, highlighting its technical innovations, unique training methods, and modular architecture. Learners will explore DeepSeek's reinforcement learning strategies, MoE design, multilingual training data, and deployment options. The module also examines DeepSeek's impact on the global AI landscape and its role in advancing technological maturity.

Deep Dive into DeepSeek

This module explores the inner workings of DeepSeek, focusing on its technical architecture, innovative attention mechanisms, and unique reasoning capabilities. Learners will examine how DeepSeek achieves efficient performance, supports agentic reasoning, and addresses challenges in interpretability and alignment. Practical insights into training strategies and model distillation are also provided.

Prompting DeepSeek

This module introduces the unique mental models and prompting strategies required to effectively use DeepSeek's R-series and V-series language models. Learners will explore structured prompting techniques, understand model-specific behaviors, and gain practical skills for troubleshooting and migrating prompts from other platforms. By the end, you'll be equipped to generate reliable, machine-readable outputs and optimize DeepSeek's advanced features for your applications.