AI Development with DeepSeek for Developers

Overzicht

The AI Development with DeepSeek for Developers course is a hands-on guide to building real-world AI applications using DeepSeek's open-source models—no prior experience required. Designed for developers, students, ML engineers, and tech enthusiasts, this course combines conceptual overviews with practical projects, delivered through Jupyter notebooks and popular Python libraries like LangChain, Streamlit, Promptify, DSPy, and DeepEval.

Start by exploring DeepSeek’s ecosystem, accessing V3, R1, and Coder models via web and API, and building your first Q&A app. Next, apply DeepSeek to real-world tasks—summarizing documents, generating code, building interactive Q&A systems, and advanced reasoning.

Then, create task-specific AI agents, compare DeepSeek’s performance and pricing with leading LLMs, and interpret benchmark results. Finally, explore DeepSeek’s unique features—GRPO, Mixture-of-Experts architecture, quantized models, and fine-tuning options for custom use cases.

By the end, you will be able to:

- Build Q&A, chatbot, and code generation apps using DeepSeek and Python tools. - Apply DeepSeek for summarization, coding, and advanced reasoning in real projects. - Create and compare AI agents, analyze model performance, and interpret benchmarks. - Customize and evaluate DeepSeek models for your own use cases, leveraging open-source features. Whether you’re starting your AI journey or expanding your toolkit, this course will help you confidently build and deploy practical AI solutions with DeepSeek.

Lesprogramma

• Getting Started with DeepSeek Models
In the first module, you’ll get introduced to DeepSeek’s open-source model ecosystem and learn how to access and use the models via the web interface and API. You’ll explore the differences between DeepSeek-V3, R1, and Coder, and build your first simple app using Promptify and Streamlit. This module builds your foundation for hands-on work with the models.
• Applications of DeepSeek Models
This module focuses on applying DeepSeek to solve real-world tasks. You’ll use DeepSeek models for document retrieval, summarization, code generation, and building interactive Q&A systems. This module blends conceptual understanding with implementation using LangChain, DSPy, and Jupyter notebooks.
• Agents and Model Comparisons with DeepSeek
In the third module, you’ll build practical task-based agents using DeepSeek and LangChain. You’ll also evaluate how DeepSeek compares with other leading models like GPT-4o, Claude, and Gemini in terms of pricing, benchmark performance, and capabilities. This module gives you insight into model selection and real-world deployment tradeoffs.
• What’s Different About DeepSeek
In the final module, you’ll explore DeepSeek’s unique innovations — from Guided Roleplay Optimization (GRPO) and Mixture-of-Experts to fine-tuning and quantized models for efficient inference. You’ll also use tools like DeepEval to assess model output quality and benchmark performance. This module helps you understand DeepSeek’s strengths and customization potential.

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Gegeven door

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Vakgebieden

Computer Science