Large Language Models (LLMs) are transforming the way organizations interact with data, automate tasks, and deliver personalized experiences. This course unpacks the architecture, training methods, and strategic implementation of LLMs—core skills for anyone looking to thrive in the evolving AI landscape.
Decoding Large Language Models
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您将学到什么
Explore the architecture and components of modern large language models
Implement and manage LLMs effectively in organizational settings
Master techniques for training, fine-tuning, and deploying LLMs
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要了解的详细信息
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November 2025
作业
15 项作业
授课语言：英语（English）
了解顶级公司的员工如何掌握热门技能
该课程共有15个模块
In this section, we explore LLM architecture, focusing on Transformer models, attention mechanisms, and their advantages over RNNs, enhancing understanding of modern language systems.
涵盖的内容
2个视频9篇阅读材料1个作业
In this section, we examine how LLMs use probability and statistical analysis for decision-making, focusing on mechanisms, challenges, and practical implications for model reliability and accuracy.
涵盖的内容
1个视频6篇阅读材料1个作业
In this section, we explore data preparation, training environment setup, and hyperparameter tuning for LLMs, emphasizing balanced datasets and strategies to address overfitting and underfitting.
涵盖的内容
1个视频6篇阅读材料1个作业
In this section, we explore transfer learning, curriculum learning, and multitasking to enhance LLM performance, focusing on practical applications and real-world adaptability.
涵盖的内容
1个视频8篇阅读材料1个作业
In this section, we explore techniques like LoRA and PEFT to enhance LLM adaptability for NLP tasks, focusing on efficient fine-tuning and precision in model customization for real-world applications.
涵盖的内容
1个视频8篇阅读材料1个作业
In this section, we explore methods for evaluating LLMs using quantitative metrics, human-in-the-loop protocols, and ethical bias analysis to ensure reliable and responsible model performance.
涵盖的内容
1个视频7篇阅读材料1个作业
In this section, we explore deploying LLMs in production, focusing on scalability, security, and maintenance to ensure reliable and efficient real-world performance.
涵盖的内容
1个视频7篇阅读材料1个作业
In this section, we examine strategies for integrating LLMs into existing systems, focusing on compatibility, security, and practical implementation techniques.
涵盖的内容
1个视频8篇阅读材料1个作业
In this section, we explore quantization, pruning, and knowledge distillation to optimize LLMs for efficiency and performance in real-world applications.
涵盖的内容
1个视频7篇阅读材料1个作业
In this section, we cover hardware acceleration, data optimization, and cost-performance balance for LLM deployment.
涵盖的内容
1个视频5篇阅读材料1个作业
In this section, we examine LLM vulnerabilities, bias mitigation strategies, and legal compliance challenges, emphasizing responsible AI deployment and ethical decision-making.
涵盖的内容
1个视频7篇阅读材料1个作业
In this section, we explore the use of LLMs in customer service, marketing, and operations, highlighting their role in improving efficiency, optimizing strategies, and delivering measurable ROI through automation and data analysis.
涵盖的内容
1个视频5篇阅读材料1个作业
In this section, we examine the selection and integration of LLM tools, comparing open source and proprietary options, and highlight the role of cloud services in NLP workflows.
涵盖的内容
1个视频6篇阅读材料1个作业
In this section, we cover GPT-5 readiness, contextual understanding, and strategic planning for future LLM advancements.
涵盖的内容
1个视频6篇阅读材料1个作业
In this section, we review key insights and explore the future of LLMs and AI learning opportunities.
涵盖的内容
1个视频3篇阅读材料1个作业
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