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Introduction to DeepSeek

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Introduction to DeepSeek

Instructeurs : Brian Newman

Inclus avec

3 modules

Obtenez un aperçu d'un sujet et apprenez les principes fondamentaux.

niveau Intermédiaire

Expérience recommandée

3 heures à compléter

Planning flexible

Apprenez à votre propre rythme

3 modules

Obtenez un aperçu d'un sujet et apprenez les principes fondamentaux.

niveau Intermédiaire

Expérience recommandée

3 heures à compléter

Planning flexible

Apprenez à votre propre rythme

Ce que vous apprendrez

Analyze key features, architectural differences, and competitive positioning of DeepSeek-V3 and DeepSeek-R1 models against proprietary alternatives.
Implement practical applications using DeepSeek models through web interfaces, APIs, and local deployment methods.
Evaluate real-world use cases for DeepSeek across industries, including content generation, reasoning tasks, and automation workflows.
Apply cost-effective strategies for integrating DeepSeek models into existing workflows while understanding limitations and best practices.

Compétences que vous acquerrez

Catégorie : AI Integrations
Catégorie : Case Studies
Catégorie : Integration Testing
Catégorie : Automation
Catégorie : Application Deployment
Catégorie : LLM Application
Catégorie : Program Evaluation
Catégorie : Strategic Planning
Catégorie : Process Optimization
Catégorie : Cost Benefit Analysis
Catégorie : Generative Model Architectures
Catégorie : AI literacy
Catégorie : Competitive Analysis
Catégorie : Responsible AI
Catégorie : Data Ethics
Catégorie : Model Evaluation

Outils que vous découvrirez

Catégorie : DeepSeek API
Catégorie : Model Deployment
Catégorie : Deepseek
Catégorie : OpenAI

Détails à connaître

Certificat partageable

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Il y a 3 modules dans ce cours

Did you know that a single AI model release wiped out over $1 trillion in global market value in just one day? As per Reuters, the launch of DeepSeek's R1 model in January 2025 triggered the largest tech selloff in history, with Nvidia alone losing $600 billion. This Chinese startup achieved something that shocked Silicon Valley: they built an AI model rivaling OpenAI's best at a small fraction of the cost and in a fraction of the time.

Welcome to the world of DeepSeek, where open-source innovation meets high performance. This beginner-friendly course will take you from a curious observer to a confident user. We’ll walk through the basics, explain how the Mixture-of-Experts architecture works, and show you how to use DeepSeek tools in your projects. You'll discover exactly how DeepSeek's reasoning models outperform industry-leading AI systems on mathematical tasks while costing 95% less to run. We'll walk you through three different ways to access these models: the free web interface, API integration, and running them locally on your hardware. This course is designed for a diverse group of learners, including developers and AI enthusiasts who are curious about open-source alternatives to proprietary models. It’s equally valuable for data scientists exploring cost-effective reasoning models for R&D, as well as business professionals seeking to leverage AI for automation and content generation. Students and researchers with a passion for cutting-edge AI architectures will also benefit from the hands-on insights provided throughout the course. While this course is beginner-friendly, a basic understanding of artificial intelligence and machine learning concepts will help learners grasp core ideas more quickly. Familiarity with APIs and web-based tools is recommended, as the course covers accessing DeepSeek models through different interfaces. Some exposure to command-line tools is helpful—especially for those opting for local deployment—but is not strictly required. By the end of this course, learners will be able to critically analyze the architecture and capabilities of DeepSeek models like V3 and R1 and how they stack up against proprietary offerings. They’ll gain the skills to implement DeepSeek in practical scenarios using web tools, APIs, and local environments. Learners will also explore real-world use cases—from content generation to automation—and apply strategies for cost-effective integration while accounting for limitations and best practices in deployment.

Détails du module

In this course, you’ll explore the architecture, functions, and real-world applications of DeepSeek’s open-source AI models. Through hands-on experience using web interfaces, APIs, and local deployments, you’ll learn to implement DeepSeek for tasks like content generation, data analysis, and reasoning. You’ll also compare model performance, evaluate cost-effective integration strategies, and understand the broader impact of open-source AI on the tech landscape.

Inclus

4 vidéos1 lecture1 devoir

4 vidéosTotal 22 minutes

Meet Your Course Guide3 minutes
The DeepSeek Revolution 4 minutes
Understanding Mixture-of-Experts Architecture in DeepSeek 5 minutes
V3 vs R1: Choosing the Right DeepSeek Model 9 minutes

1 lectureTotal 5 minutes

Complete DeepSeek Model Guide 5 minutes

1 devoirTotal 30 minutes

Hands On Learning (HOL): DeepSeek Comparative Model Analysis Challenge 30 minutes

In this lesson, we shift from theory to practice, guiding you through the nuts and bolts of accessing, configuring, and deploying DeepSeek models in real-world environments. Whether you're working with open-source variants or enterprise-grade integrations, you'll learn how to navigate model selection, API access, and prompt engineering with confidence. By the end of this module, you’ll not only understand how DeepSeek works—you’ll be using it to solve problems, test ideas, and build smarter workflows.

Inclus

3 vidéos1 lecture1 devoir

This lesson explores how DeepSeek can be strategically implemented across industries—from education and healthcare to finance and creative production. You’ll analyze case studies, evaluate deployment strategies, and consider ethical and operational factors that shape successful adoption. The goal isn’t just to use DeepSeek—it’s to use it wisely, aligning its capabilities with your organizational goals, user needs, and long-term vision.