【Angular】用Angular开发释放AI和ML的力量：初学者指南
在动态的web开发世界中，人工智能（AI）和机器学习（ML）与Angular的融合代表了向创建更智能、响应更快、以用户为中心的应用程序的突破性转变。这种集成不仅有望增强用户体验，还可以自动化复杂的流程，从而为web应用程序的未来设定新的标准。本详细指南专为热衷于保持领先地位的软件工程师、开发人员和技术爱好者而设计，逐步揭示了将AI和ML与Angular开发集成的过程。
引言：拥抱Web开发的未来
随着数字技术的不断发展，在Angular框架内集成AI和ML对于旨在构建尖端web应用程序的开发人员来说变得越来越重要。Angular凭借其强大的架构和易用性，为整合AI和ML功能提供了理想的基础。本指南旨在让初学者清楚、实用地了解如何将AI和ML与Angular开发无缝融合，改变应用程序交互的方式，从用户行为中学习，并自动化任务。
【Angular】使用Google Gemini构建AI驱动的Angular应用程序
【人工智能资源】海量免费资源学习人工智能
海量免费资源学习人工智能
程序设计语言
1.Python：-https://www.mygreatlearning.com/academy/learn-for-free/courses/artificial-intelligence-with-python
2.R编程：-https://www.codecademy.com/learn/learn-r
3.Java：-https://intellipaat.com/academy/course/java-training/
4.JavaScript：-https://lnkd.in/e8uxQ5
数学基础
1.线性代数：-https://lnkd.in/gMBSWaEf
2.概率统计：-https://lnkd.in/dEg3Xfpw
3.微积分：https://lnkd.in/dbCuYss5
4.离散数学：https://lnkd.in/gH-yKpHh
数据处理
1.熊猫和麻木：-https://lnkd.in/g_HwJiBJ
2.SQLite：-https://lnkd.in/gkYn9gXK
3.MongoDB：-https://lnkd.in/grxkpizU
langchain支持PGVector作为矢量存储
使用postgres作为后端并利用pgvector扩展的LangChain向量库抽象的实现。
The code lives in an integration package called: langchain_postgres.
您可以运行以下命令来启动具有pgvector扩展名的postgres容器：
Langchain库中的OpenAI函数调用API
检索增强一代的终结？新兴的体系结构标志着一种转变
Retrieval Augmented Generation (RAG) has been a cornerstone in enhancing large language models (LLMs) for complex, knowledge-driven tasks. By pulling in relevant data from a vector database, RAG has empowered LLMs with factual grounding, significantly reducing instances of fabricated information. But is this the end of the road for RAG?
Devin，新的人工智能，能取代人类软件工程师吗？
A new AI named Devin claiming the title of the world’s first AI software engineer. From coding entire projects to fixing GitHub issues, Devin seems to be the new topic. And with such sensational capabilities, the rumor mill is working overtime, sparking fears that the era of human software engineers might be coming to an end. But before you join the panic parade, let’s take a look and see why, despite these advancements, we’re not heading for the job market exit anytime soon.
使用LangChain、LLM和Streamlit构建用于复杂SQL数据库交互的聊天应用程序
In this article we will see how we can use large language models (LLMs) to interact with a complex database using Langchain agents and tools, and then deploying the chat application using Streamlit.
如何从头开始构建LLM
This is the 6th article in a series on using large language models (LLMs) in practice.
用LangChain和Amazon Bedrock建立RAG管道
In this post, you'll learn how you can set up and integrate Amazon Bedrock with your LangChain app for an end-to-end RAG pipeline
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New to LangChain? Start with this introductory post first. It'll give you a great overview of everything you need to know before diving in. Come back when you're done!