个结果，搜索词为 "llm application"
DeepLearning.AI
您将获得的技能: LangChain, LLM Application, Large Language Modeling, Retrieval-Augmented Generation, Generative AI Agents, Prompt Engineering, Generative AI, Generative Model Architectures, AI Workflows
您将获得的技能: LangChain, LLM Application, Large Language Modeling, AI Workflows, Retrieval-Augmented Generation, Embeddings, Generative AI, Tool Calling, Fine-tuning, Prompt Engineering, PyTorch (Machine Learning Library), Tensorflow, Semantic Web, Natural Language Processing, Artificial Intelligence, Application Development, Analytical Skills, Application Programming Interface (API), Supervised Learning, Computer Programming Tools
您将获得的技能: LangChain, LLM Application, Hugging Face, Retrieval-Augmented Generation, Prompt Patterns, LangGraph, Model Deployment, AI Workflows, Large Language Modeling, AI Orchestration, Generative AI, Token Optimization, Generative Model Architectures, AI Integrations, Fine-tuning, Embeddings, Data Pipelines, Containerization, Restful API, Docker (Software)
您将获得的技能: LangChain, LLM Application, Prompt Engineering, API Design, MLOps (Machine Learning Operations), Large Language Modeling, Performance Tuning, Microservices, Application Deployment, Kubernetes, Scalability, Site Reliability Engineering, DevOps, CI/CD, Docker (Software), Python Programming, Containerization, System Monitoring, Cloud Platforms, Security Controls
您将获得的技能: Hugging Face, Prompt Engineering, Retrieval-Augmented Generation, LLM Application, Model Evaluation, Responsible AI, Fine-tuning, LangChain, Model Optimization, Large Language Modeling, Generative Model Architectures, Model Deployment, Generative AI, Generative Adversarial Networks (GANs), Application Deployment, Application Development, AI literacy, Technical Management, Business Strategy, Python Programming
您将获得的技能: Retrieval-Augmented Generation, OpenAI API, Prompt Patterns, AI Workflows, Generative Model Architectures, Fine-tuning, Model Deployment, Token Optimization, Applied Machine Learning, Hugging Face, Programming Principles, Application Deployment, Memory Management, Object Oriented Programming (OOP), Context Management, API Testing, Document Management, Model Optimization, Embeddings, Software Installation
是什么让您今天来到 Coursera？
Google Cloud
您将获得的技能: Prompt Engineering, 生成式人工智能, 大型语言建模, 及时工程, 法学硕士申请
DeepLearning.AI
您将获得的技能: Responsible AI, LLM Application, Large Language Modeling, Google Cloud Platform, Model Training, MLOps (Machine Learning Operations), Fine-tuning, Model Evaluation, Version Control, Supervised Learning
DeepLearning.AI
您将获得的技能: Python 编程, 大型语言建模, LangChain, 法学硕士申请, 检索-增强生成, JavaScript 框架, 人工智能协调
您将获得的技能: Prompt Engineering, LLM Application, OpenAI API, OpenAI, Large Language Modeling, Agentic systems, Hugging Face, Embeddings, AI Integrations, Application Development, Data Processing, Generative AI Agents, Application Frameworks, Memory Management
您将获得的技能: Retrieval-Augmented Generation, LLM Application, Large Language Modeling, MLOps (Machine Learning Operations), Fine-tuning, Model Deployment, Extract, Transform, Load, Data Pipelines, Model Optimization, Data Processing, CI/CD, Model Evaluation
您将获得的技能: MLOps (Machine Learning Operations), LLM Application, AI Security, Test Automation, Test Script Development, Test Driven Development (TDD), Unit Testing, CI/CD, Continuous Integration, Software Testing, Testability, Large Language Modeling, Model Deployment, Test Case, Responsible AI, Security Testing, AI Workflows, Python Programming, Statistical Analysis, SQL
总之，以下是 10 最受欢迎的 llm application 课程
- LangChain for LLM Application Development: DeepLearning.AI
- LLM Application Engineering and Development Certification: Simplilearn
- Building LLMs with Hugging Face and LangChain: Edureka
- Build Next-Gen LLM Apps with LangChain & LangGraph: Coursera
- Harnessing LLMs: Strategy, Fine-Tuning & Evaluation: Coursera
- AI & LLM Engineering Mastery - GenAI, RAG Complete Guide: Packt
- 大型语言模型介绍: Google Cloud
- LLMOps: DeepLearning.AI
- 使用 LangChain.js 构建 LLM 应用程序: DeepLearning.AI
- LangChain MasterClass: Build 15 LLM Apps with Python: Packt
