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所在平台: Udemy |
课程主页: https://www.udemy.com/course/mastering-deepscaler-build-deploy-ai-models-with-ollama/
课程评论：没有评论
课程名称: 精通 DeepScaleR：使用 Ollama 构建与部署 AI 模型 课程概述: 《精通 DeepScaleR 与 Ollama》是您学习如何在本地构建、微调和部署 AI 模型的绝佳途径，不需要依赖昂贵的云 API。该课程将通过实践教学，帮助您利用开源 AI 的能力创建在本地机器上运行的智能应用。您将学习如何使用优化数学推理、代码生成和 AI 自动化的 DeepScaler（一种深度调优的 DeepSeek-R1-Distilled-Qwen-1.5B 版本），以及如何利用 Ollama 实现高效、经济的本地 AI 模型部署。 本课程旨在将您从初学者培养为高级 AI 开发者。您将从在 Mac、Windows（WSL）或 Linux 上设置 DeepScaler 和 Ollama 开始，然后学习如何在本地运行 AI 模型，从而消除对云 API 的需求。您将使用 DeepScaler 构建一个功能齐全的 AI 聊天机器人，并通过 FastAPI 部署它。同时，您还将开发一个 AI 驱动的数学求解器，能够实时解决复杂方程。 课程的一个主要重点是使用 LoRA 和 QLoRA 对 DeepScaler 进行微调。您将使用自定义数据集训练 DeepScaler，以提升响应质量，并将模型适应特定领域的任务，例如金融、医疗和法律分析。此外，课程还将指导您构建一个 AI 驱动的代码助手，该助手能够高效生成、调试和解释代码。 课程还将关注 AI 模型的优化，以实现低延迟响应。您将学习如何提高 AI 推理速度，并将 DeepScaler 的性能与 OpenAI 的 o1-preview 进行比较。课程中还会介绍 Gradio，这是一个可以创建互动 AI 驱动的 web 应用程序的工具，使得在用户友好的界面中更容易部署和测试 AI 模型。 本课程非常适合希望学习如何在没有云依赖的情况下部署 AI 模型的 AI 开发者、软件工程师、数据科学家和技术爱好者。同时，它也是希望在无须深度学习经验的情况下开始本地 AI 模型开发的学生和初学者的理想选择。与传统的 AI 开发相比，本地 AI 部署提供了更高的隐私、安全性和控制权。借助 DeepScaler 和 Ollama，您能够在自己的设备上运行 AI 模型，而无需承担 API 成本或依赖第三方云服务，从而实现实时 AI 应用，响应速度更快，效率更高。 通过本课程的学习，您将能够在本地运行多个 AI 应用，并针对特定用例进行模型的微调。无论您是构建聊天机器人、数学求解器、代码助手，还是 AI 驱动的自动化工具，本课程将为您提供必要的知识和实践经验，帮助您有效地开发、微调和部署 AI 模型。无需 AI 经验，如果您对 LLM 微调、AI 聊天机器人开发、代码生成、AI 驱动的自动化以及本地 AI 模型部署感兴趣，这门课程将为您提供掌握这些技能的工具和专业知识。
Mastering DeepScaler and Ollama is your gateway to building, fine-tuning, and deploying AI models locally without relying on expensive cloud APIs. This hands-on course will teach you how to harness the power of open-source AI to create intelligent applications that run on your own machine. You will learn how to work with DeepScaler, a fine-tuned version of DeepSeek-R1-Distilled-Qwen-1.5B, optimized for math reasoning, code generation, and AI automation, while Ollama enables seamless local AI model deployment for efficient and cost-effective AI applications. (AI)This course is designed to take you from beginner to advanced AI development. You will start by setting up DeepScaler and Ollama on Mac, Windows (WSL), or Linux. From there, you will learn how to run AI models locally, eliminating the need for cloud-based APIs. You will build a fully functional AI chatbot using DeepScaler and deploy it via FastAPI. You will also develop an AI-powered Math Solver that can solve complex equations in real time.A major focus of the course is fine-tuning DeepScaler using LoRA and QLoRA. You will train DeepScaler on custom datasets to improve responses and adapt the model to domain-specific tasks such as finance, healthcare, and legal analysis. The course will also guide you through building an AI-powered Code Assistant, which can generate, debug, and explain code efficiently.One of the most important aspects of working with AI models is optimization for low-latency responses. You will learn how to improve AI inference speed and compare DeepScaler's performance against OpenAI's o1-preview. The course will also introduce Gradio, a tool that allows you to create interactive AI-powered web applications, making it easier to deploy and test AI models in a user-friendly interface.This course is ideal for AI developers, software engineers, data scientists, and tech enthusiasts who want to learn how to deploy AI models without cloud dependencies. It is also a great choice for students and beginners who want to get started with local AI model development without requiring prior deep learning experience.Unlike traditional AI development, local AI deployment provides greater privacy, security, and control. With DeepScaler and Ollama, you will be able to run AI models on your device without incurring API costs or depending on third-party cloud services. This enables real-time AI-powered applications with faster response times and better efficiency.By the end of this course, you will have multiple AI-powered applications running locally with models fine-tuned for specific use cases. Whether you are building a chatbot, a math solver, a code assistant, or an AI-powered automation tool, this course will provide you with the knowledge and hands-on experience needed to develop, fine-tune, and deploy AI models effectively.No prior AI experience is required. If you are interested in LLM fine-tuning, AI chatbot development, code generation, AI-powered automation, and local AI model deployment, this course will give you the tools and expertise to master these skills.
