Description

DeepSeek AI is a cutting-edge open-source Large Language Model (LLM) designed for various AI applications, including text generation, code completion, and business automation. As AI technology evolves, understanding how to leverage powerful open-source models like DeepSeek can provide cost-effective and efficient solutions compared to proprietary models like OpenAI's ChatGPT.

This course is designed to provide a comprehensive, hands-on experience with DeepSeek AI. You will learn how DeepSeek works, how it differs from ChatGPT, and how to use it effectively for real-world applications. By the end of the course, you will also build a functional AI-powered application using DeepSeek’s distilled models.

Course Objectives

Understand what DeepSeek AI is and how it fits into the open-source AI ecosystem

Learn how DeepSeek compares with OpenAI’s ChatGPT in terms of architecture, capabilities, and use cases

Gain hands-on experience using DeepSeek for text generation, coding assistance, and business automation

Learn how to fine-tune and customize DeepSeek models for domain-specific applications

Build a real-world AI-powered application using DeepSeek’s distilled models

Explore deployment and scaling strategies for AI applications

Understand best practices for implementing DeepSeek in different use cases

Target Audience

AI Enthusiasts – Individuals eager to explore AI and LLMs for the first time

Developers & Programmers – Software engineers and developers interested in integrating AI into their applications

Data Scientists & Analysts – Professionals looking to leverage AI for data-driven solutions

Business Professionals – Entrepreneurs and decision-makers exploring AI-powered automation

Content Creators & Marketers – Individuals interested in using AI for content generation and marketing automation

Students & Researchers – Individuals who want to dive into the world of AI with practical knowledge

Basic Understanding

General AI/ML Awareness – Understanding of AI concepts (not mandatory, but helpful)

Basic Computer Skills – Familiarity with using web-based tools and APIs

Basic Python (Optional but Useful) – While no advanced coding is required, familiarity with Python can help in hands-on sections

No Prior LLM Experience Required – The course will cover everything from scratch

Course Content

No sessions available.

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Mastering DeepSeek – Understanding, Comparing, and Building AI Applications

Session 1: Introduction to DeepSeek AI

  1. What is DeepSeek?

    Overview of DeepSeek models

    Understanding the open-source AI landscape

    Use cases and applications

  2. DeepSeek vs OpenAI’s ChatGPT

    Model architecture and training differences

    Strengths and weaknesses

    Cost-effectiveness and deployment options

  3. Setting Up DeepSeek

    Accessing DeepSeek models

    API keys and integrations

    Local vs cloud-based deployment

Session 2: Hands-on with DeepSeek

  1. Exploring DeepSeek’s Capabilities

    Text generation and completion

    Code assistance and debugging

    Data analysis use cases

  2. Fine-tuning and Customization

    Prompt engineering with DeepSeek

    Adapting models for domain-specific applications

    Comparison of responses with ChatGPT

  3. DeepSeek in Real-World Applications

    Using DeepSeek for content generation

    AI-powered chatbots

    Automation in business workflows

Session 3: Building an Application Using DeepSeek Distilled Models

  1. Introduction to DeepSeek Distilled Models

    What are distilled models?

    Performance vs full-scale models

    Choosing the right model for your needs

  2. Setting Up the Development Environment

    Required tools and libraries (Python, API access, etc.)

    Installing and integrating DeepSeek models

  3. Building a Simple AI-Powered Application

    Defining the project scope (e.g., AI chatbot, text summarization tool)

    Writing the backend logic with DeepSeek API

    Implementing a simple front-end interface

  4. Deploying and Scaling the Application

    Hosting options (Cloud, Server, or Local)

    Performance optimization

    Future enhancements and adding new features

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