Dive deep into DeepSeek’s architecture, core capabilities, and real-world applications with this advanced deepseek course designed for developers and AI professionals. You’ll explore the foundational DeepSeek AI models, groundbreaking innovations like Efficient Mixture of Experts (MoE) and Multi-Head Latent Attention (MLA), and gain hands-on experience with practical integration via API and local deployment.



Expérience recommandée
Ce que vous apprendrez
Understand DeepSeek’s architecture, training strategies, and key innovations for scalable AI solutions.
Deploy and integrate DeepSeek AI models through APIs and local hosting for real-world applications.
Build and fine-tune DeepSeek models for advanced reasoning, text generation, and intelligent automation.
Apply DeepSeek in diverse domains, from workflow automation to RAG systems and custom AI-powered apps.
Compétences que vous acquerrez
- Catégorie : Unstructured Data
- Catégorie : Scalability
- Catégorie : Generative AI
- Catégorie : Application Programming Interface (API)
- Catégorie : Automation
- Catégorie : Systems Architecture
- Catégorie : Open Source Technology
- Catégorie : Application Deployment
- Catégorie : Prompt Engineering
- Catégorie : Software Development Tools
- Catégorie : Integration Testing
- Catégorie : Deep Learning
- Catégorie : Large Language Modeling
- Catégorie : Generative AI Agents
- Catégorie : Reinforcement Learning
- Catégorie : Applied Machine Learning
- Catégorie : Software Architecture
- Catégorie : Development Environment
- Catégorie : Business Process Automation
- Catégorie : Artificial Intelligence
Détails à connaître

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mai 2025
15 devoirs
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Il y a 5 modules dans ce cours
This module lays the groundwork for understanding DeepSeek’s capabilities, technical innovations, and practical access methods. It starts by introducing the strategic importance of DeepSeek in the broader AI landscape and provides a comparative look at its core models like V3, R1, and Janus Pro. Learners will gain a deeper appreciation for what sets DeepSeek apart in terms of performance, transparency, and cost-effectiveness. The module also walks through access methods—including web, local, API, and third-party interfaces—and addresses widespread myths related to DeepSeek’s origin, development cost, and security concerns. By the end, learners will have a clear understanding of how to access, evaluate, and use DeepSeek effectively and responsibly.
Inclus
13 vidéos3 lectures3 devoirs1 sujet de discussion
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.
Inclus
10 vidéos2 lectures3 devoirs
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.
Inclus
11 vidéos2 lectures3 devoirs
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.
Inclus
11 vidéos2 lectures3 devoirs
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.
Inclus
10 vidéos2 lectures3 devoirs
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Foire Aux Questions
This course offers a deep dive into DeepSeek’s architecture, training methods, and real-world applications. You’ll gain hands-on experience with API integration, local deployment, workflow automation, and advanced fine-tuning techniques.
It’s ideal for AI developers, software engineers, data scientists, product managers, and automation architects who want to apply DeepSeek to scalable, AI-powered solutions.
Yes. If you're new to DeepSeek, start with DeepSeek Essentials: From Foundations to Real-World Use. It covers core concepts and basic integrations before you move on to this advanced course for deeper technical mastery.
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