This module dives deep into the architectural design and technical innovations that define DeepSeek. It begins by explaining the high-level architecture of DeepSeek models and highlights their unique reasoning-centric training methodology, including the use of Reinforcement Learning from Human Feedback (RLHF). Learners will explore how DeepSeek models evolve from R1 to R1-Zero and understand the role of components like Multi-Head Latent Attention (MLA) and Mixture of Experts (MoE). By the end of this module, learners will have gained insight into how these innovations contribute to enhanced performance, scalability, and contextual understanding in AI outputs.

This module focuses on giving learners hands-on skills to deploy and integrate DeepSeek in both cloud and local environments. Learners will explore how to work with the DeepSeek API—from generating keys to integrating with automation tools such as N8N and Make.com. The second half of the module guides learners through self-hosting DeepSeek using tools like LMStudio, with step-by-step instructions on building local RAG (Retrieval-Augmented Generation) systems. Practical demonstrations ensure that learners can independently set up, manage, and troubleshoot deployments for varied use cases.

This module highlights the wide-ranging real-world use cases where DeepSeek excels. It starts by categorizing the types of tasks DeepSeek can handle—from simple content generation and classification to advanced problem-solving and reasoning. Learners will then explore how DeepSeek supports downstream tasks using embeddings and powers applications like Retrieval-Augmented Generation (RAG) and AI agents. The module wraps up with workflow automation, web/mobile app integration, and best practices for aligning DeepSeek with business or technical objectives.

This final module focuses on empowering developers with tools and techniques to enhance, adapt, and customize DeepSeek for specific software development needs. Learners will explore how to use DeepSeek for intelligent code generation, debugging, and test creation. The module also provides an end-to-end guide to fine-tuning DeepSeek models on custom datasets for specialized applications. By the end, learners will have both the foundational understanding and the practical skills to extend DeepSeek’s capabilities through customization and development-centric workflows.