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所在平台: Udemy |
课程主页: https://www.udemy.com/course/certification-in-generative-ai-models-and-tools/
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课程名称：生成性人工智能模型与工具认证课程 课程概述： 在此课程中，无论您是有志的人工智能工程师、创意专业人士、商业领袖还是人工智能爱好者，都能掌握生成性人工智能的关键概念和技术。课程介绍了如GAN（生成对抗网络）、VAE（变分自编码器）和变换器等前沿AI模型是如何在内容创作、自动化等各行业中变革的。通过本课程，您将学习到： 1. 掌握生成性人工智能的基本技能和概念，包括深度学习、神经网络和AI模型训练。 2. 使用开源库和框架构建和优化生成性AI模型，确保高效的AI驱动内容生成。 3. 访问行业标准工具，如ChatGPT、DALL·E、MidJourney、Stable Diffusion和Synthesia，进行实践实验。 4. 探索生成性人工智能在创意产业、自动化、医疗等领域的真实应用。 课程框架： 本课程通过生动的视频讲座、案例研究、项目、可下载资源及互动练习来深入探讨生成性人工智能，包括AI模型架构、实践应用及真实世界的AI实现。课程内容包括多个案例研究、资源模板、工作表、阅读材料、测验、自我评估及实践实验，以增强您对生成性AI的理解。 课程结构： - 第一部分：AI基础知识及学习规划 - 介绍生成性AI的基本概念、历史及其在各领域的应用。 - 第二部分：生成性AI的核心技术 - 深入了解GAN、VAE和变换器，获得使用AI工具和模型的实践体验。 - 第三部分：流行的生成性AI工具 - 探索文本、图像、视频及音频生成工具的使用。 - 第四部分：构建生成性AI模型 - 学习数据准备、GAN和变换器的训练、预训练模型的微调及模型部署。 - 第五部分：生成性AI的应用案例 - 分析创意内容生成、代码生成、个性化推荐及医疗应用等。 - 第六部分：伦理考虑与挑战 - 讨论生成性模型中的偏见、知识产权、安全风险及环境影响。 - 第七部分：生成性AI的未来 - 探讨新兴趋势、潜在创新及在生成性AI领域的职业机会。 投资时间学习生成性AI，为创建和管理驱动创新的AI解决方案打下基础。
DescriptionTake the next step in your AI journey! Whether you're an aspiring AI engineer, a creative professional, a business leader, or an AI enthusiast, this course will help you master the key concepts and technologies behind Generative AI. Learn how cutting-edge AI models like GANs, VAEs, and Transformers are transforming industries, from content creation to automation and beyond.With this course as your guide, you learn how to:Master the fundamental skills and concepts required for Generative AI, including deep learning, neural networks, and AI model training.Build and optimize Generative AI models using open-source libraries and frameworks, ensuring efficient AI-driven content generation.Access industry-standard tools such as ChatGPT, DALL·E, MidJourney, Stable Diffusion, and Synthesia for hands-on experimentation.Explore real-world applications of Generative AI in creative industries, automation, healthcare, and more.Invest in learning Generative AI today and gain the skills to create and manage AI-powered solutions that drive innovation.The Frameworks of the CourseEngaging video lectures, case studies, projects, downloadable resources, and interactive exercises- this course is designed to explore Generative AI, covering AI model architectures, practical applications, and real-world AI implementations.The course includes multiple case studies, resources such as templates, worksheets, reading materials, quizzes, self-assessments, and hands-on labs to deepen your understanding of Generative AI.In the first part of the course, you'll learn the foundations of AI, machine learning, and deep learning, along with the history and evolution of Generative AI.In the middle part of the course, you'll develop a deep understanding of GANs, VAEs, and Transformers, gaining hands-on experience with AI-powered tools and models.In the final part of the course, you'll explore the ethical considerations, real-world applications, and future trends in Generative AI, along with career opportunities in AI development and research. Course Content:Part 1Introduction and Study Plan· Introduction and know your instructor· Study Plan and Structure of the CourseModule 1. Introduction to Generative AI1.1. Overview of Artificial Intelligence and Machine Learning1.2. What is Generative AI?1.3. History and Evolution of Generative AI1.4. Applications of Generative AI in Various Fields1.5. Activity: Group discussion on popular generative AI use cases (e.g., ChatGPT, DALL·E, MidJourney)1.6. ConclusionModule 2. Core Technologies Behind Generative AI2.1. Neural Networks and Deep Learning Basics2.2. Introduction to Generative Adversarial Networks (GANs)2.3. Variational Autoencoders (VAEs)2.4. Transformers and Language Models (e.g., GPT, BERT)2.5. Activity: Hands-on experiment with a pre-trained model (e.g., GPT-3)2.6. ConclusionModule 3. Popular Generative AI Tools3.1. Text Generation Tools (ChatGPT, Jasper AI, Writesonic)3.2. Image Generation Tools (DALL E, MidJourney, Stable Diffusion)3.3. Video and Audio Generation Tools (Synthesia, Runaway ML, Resemble AI)3.4. Coding and Development Tools (GitHub Copilot, Tabnine)3.5. Activity: Practical exercises with tools like DALL E or ChatGPT3.6. ConclusionModule 4. Building Generative AI Models4.1. Data Preparation and Preprocessing4.2. Training GANs and Transformers4.3. Fine-tuning Pre-trained Models4.4. Deployment of Generative Models4.5. Activity: Build a simple text generator or image generator using Python and open-source libraries4.6. ConclusionModule 5. Use Cases of Generative AI5.1. Creative Content Generation (e.g., art, writing, video)5.2. Code Generation and Automation5.3. Personalized Recommendations5.4. Healthcare Applications (e.g., drug discovery, diagnosis aids)5.5. Activity: Case study analysis: Real-world applications of generative AI.5.6. ConclusionModule 6. Ethical Considerations and Challenges6.1. Bias in Generative Models6.2. Intellectual Property Concerns6.3. Security Risks (e.g., deepfakes)6.4. Addressing Environmental Impact (e.g., energy consumption of training models)6.5. Activity: Debate or panel discussion on ethical concerns in generative AI.6.6. ConclusionModule 7. Future of Generative AI7.1. Emerging Trends in Generative AI7.2. Potential Innovations in Tools and Applications7.3. Career Opportunities in Generative AI7.4. Activity: Research project: Predicting the future impact of generative AI on a specific industry.7.5. Conclusion
