Ollama & DeepSeek Reasoning Model Masterclass.

Format:

Video

Language:

English

Physical Description:

1 online resource (104 minutes)

Place of Publication:

[Place of publication not identified] : PACKT Publishing, 2025.

Language Note:

In English.

System Details:

video file

Summary:

<b>Master local AI development using Ollama and DeepSeek R1 through hands-on projects, installations, and coding walkthroughs designed for developers and engineers building privacy-focused applications.</b><h4>Key Features</h4><ul><li>Designed for developers seeking full control over AI model execution and data privacy</li><li>Focuses on real-world applications that showcase DeepSeek R1’s power in offline scenarios</li><li>Combines in-depth theory with live demonstrations to reinforce local AI development skills</li></ul><h4>Book Description</h4>Begin your journey into private AI development by preparing your local environment and understanding the full scope of what you’ll build. The course starts with an introduction to DeepSeek R1 and Ollama, outlining prerequisites and setting up the development stack. You’ll gain a solid grasp of the course structure, learning objectives, and the tools you’ll need to run large language models on your own hardware. Next, you’ll dive into the core of DeepSeek R1, exploring its architecture, model variations, and the key distinctions between open and closed-source LLMs. Through guided installations, you'll set up LM Studio and Ollama, download the R1 distilled model, and interact with it via a local chat interface. Practical hands-on sessions walk you through using the Ollama API to embed DeepSeek directly into custom applications. In the final phase, you’ll build three end-to-end AI projects: a document analyzer, a code assistant, and a multi-agent system. Each use case is presented with a functional demo followed by a complete code walkthrough. By course completion, you’ll be able to design, deploy, and iterate intelligent, offline applications that prioritize privacy, performance, and full local control.<h4>What you will learn</h4><ul><li>Deploy DeepSeek R1 using Ollama and LM Studio locally</li><li>Integrate local LLM models via Ollama API in applications</li><li>Analyze DeepSeek architecture and model distinctions</li><li>Develop private AI chatbots and assistants from scratch</li><li>Build document processing tools using LLM capabilities</li><li>Create multi-agent AI systems for parallel task execution</li></ul><h4>Who this book is for</h4>This course is ideal for AI engineers, software developers, and hobbyists who want to explore offline LLM deployment. A working knowledge of Python, REST APIs, and development environments is expected. Those seeking to build practical AI applications with complete control over data and performance will find immense value here.

Notes:

Title from resource description page (viewed July 20, 2026).