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New AI Agentic course! Learn to use LangGraph to build single and multi-agent LLM applications in AI Agents in LangGraph. This short course, taught by LangChain LangChain founder Harrison Chase Harrison Chase and Tavily founder @weiss_rotem, shows how to integrate agentic search to enhance an agent's knowledge with query-focused...

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Andrew Ng

1,304,101 subscribers

151,484 просмотров • 2 лет назад •via X (Twitter)

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Комментарии: 10

Фото профиля Arihant Parsoya
Arihant Parsoya2 лет назад

@LangChainAI @hwchase17 @tavilyai @weiss_rotem This is a great initiative to learn by doing it. We will learn how AI agents get build and different AI agents will give rise to multiple training ways. This is going to really change the way AI is developing.

Фото профиля Ryan Hidden
Ryan Hidden2 лет назад

@LangChainAI @hwchase17 @tavilyai @weiss_rotem I completed the CrewAI and AutoGen courses, and with this one, I'll have AI agents coming from all directions. AI Agent Battle Royale?

Фото профиля Manju Bhandary
Manju Bhandary2 лет назад

@LangChainAI @hwchase17 @tavilyai @weiss_rotem Thank you, Andrew! The short courses on @DeepLearningAI are awesome.

Фото профиля Payman hodaie
Payman hodaie2 лет назад

@LangChainAI @hwchase17 @tavilyai @weiss_rotem After finishing AutoGen and crewai course, I have been waiting for this course. 🙏🙏🙏

Фото профиля K3ith.AI
K3ith.AI2 лет назад

@LangChainAI @hwchase17 @tavilyai @weiss_rotem The best strikes again!!🦾🦾🦾

Фото профиля Zoe Wang
Zoe Wang2 лет назад

@LangChainAI @hwchase17 @tavilyai @weiss_rotem Thanks a lot @AndrewYNg @hwchase17 & @weiss_rotem! Just finished the Neural Networks and Deep Learning courses.

Фото профиля Archi Fussion
Archi Fussion2 лет назад

@LangChainAI @hwchase17 @tavilyai @weiss_rotem Cool love to know more..m.

Фото профиля Ethan
Ethan2 лет назад

@LangChainAI @hwchase17 @tavilyai @weiss_rotem nice

Фото профиля AiFA Labs
AiFA Labs2 лет назад

@LangChainAI @hwchase17 @tavilyai @weiss_rotem This course looks very informative. Well done! 👏

Фото профиля Kindi
Kindi2 лет назад

@LangChainAI @hwchase17 @tavilyai @weiss_rotem Looking forward to see a new OpenAI short course

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Andrew Ng

81,048 просмотров • 1 год назад

New short course: Practical Multi AI Agents and Advanced Use Cases with crewAI. Learn to build and deploy advanced agent-based systems in real applications in this course, created with CrewAI and taught by its founder, João Moura! (Disclosure: I've made a small seed investment in CrewAI.) In this course, you’ll learn how to create advanced agent-based apps that use external tools, do performance testing, can be trained with human feedback, and perform multiple tasks with different large language models. You will build several practical agentic apps that provide real business value, such as an automated project planning system, lead scoring and engagement pipeline, customer support data analysis, and a robust content creation system. In detail, you will learn how to: - Create these multi-agent systems with the building blocks of tasks, agents, and crews, along with the different things that make them work, such as caching, memory, and guardrails. - Integrate your multi-agent application with internal and external systems. - Connect multiple agents in complex setups, including parallel, sequential, and hybrid configurations, and create flows involving multiple agentic applications working together. - Test your agentic workflow and train it using human feedback to optimize its performance for better and more consistent results. - Work with multiple LLMs in your multi-agent system, using the appropriate model sizes and providers to fit each agent’s specific task. - Start a project from scratch in your environment and prepare it for deployment. You’ll also learn from an interview between João and Jacob Wilson, the Commercial GenAI Principal at PwC , in which they discuss deploying agentic workflows in real industry use cases. By the end of this course, you will be equipped to start building custom multi-agentic systems for your work. Please sign up here!
2:52

New short course: Practical Multi AI Agents and Advanced Use Cases with crewAI. Learn to build and deploy advanced agent-based systems in real applications in this course, created with CrewAI and taught by its founder, João Moura! (Disclosure: I've made a small seed investment in CrewAI.) In this course, you’ll learn how to create advanced agent-based apps that use external tools, do performance testing, can be trained with human feedback, and perform multiple tasks with different large language models. You will build several practical agentic apps that provide real business value, such as an automated project planning system, lead scoring and engagement pipeline, customer support data analysis, and a robust content creation system. In detail, you will learn how to: - Create these multi-agent systems with the building blocks of tasks, agents, and crews, along with the different things that make them work, such as caching, memory, and guardrails. - Integrate your multi-agent application with internal and external systems. - Connect multiple agents in complex setups, including parallel, sequential, and hybrid configurations, and create flows involving multiple agentic applications working together. - Test your agentic workflow and train it using human feedback to optimize its performance for better and more consistent results. - Work with multiple LLMs in your multi-agent system, using the appropriate model sizes and providers to fit each agent’s specific task. - Start a project from scratch in your environment and prepare it for deployment. You’ll also learn from an interview between João and Jacob Wilson, the Commercial GenAI Principal at PwC , in which they discuss deploying agentic workflows in real industry use cases. By the end of this course, you will be equipped to start building custom multi-agentic systems for your work. Please sign up here!

Andrew Ng

340,724 просмотров • 1 год назад

LangChain: Chat with Your Data, a new free short course created with Harrison Chase, is now available! In this 1 hour course, you’ll learn how to build one of the most requested LLM-based applications: Answering questions using information from a document or collection of documents (often called Retrieval Augmented Generation). You'll also learn how to use vector stores and embeddings to retrieve document chunks relevant to a query. I hope you enjoy the course!
3:17

LangChain: Chat with Your Data, a new free short course created with Harrison Chase, is now available! In this 1 hour course, you’ll learn how to build one of the most requested LLM-based applications: Answering questions using information from a document or collection of documents (often called Retrieval Augmented Generation). You'll also learn how to use vector stores and embeddings to retrieve document chunks relevant to a query. I hope you enjoy the course!

Andrew Ng

384,233 просмотров • 3 лет назад

🔗 Announcing LangChain OSS Skills LangChain has the most popular frameworks for building AI agents — and now your coding agent can be an expert in it. We're excited to release the first iteration of LangChain OSS Skills, giving your agent expertise in our open source frameworks. The skills include guidance on how to use langchain, langgraph, and deepagents to effectively build agents. ➡️ Install our OSS skills for your coding agent here: ➡️ Read more:
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🔗 Announcing LangChain OSS Skills LangChain has the most popular frameworks for building AI agents — and now your coding agent can be an expert in it. We're excited to release the first iteration of LangChain OSS Skills, giving your agent expertise in our open source frameworks. The skills include guidance on how to use langchain, langgraph, and deepagents to effectively build agents. ➡️ Install our OSS skills for your coding agent here: ➡️ Read more:

LangChain OSS

65,840 просмотров • 3 месяцев назад

Learn to build your own voice-activated AI assistant that can execute tasks like gathering recent AI news from the web, scripting out a podcast, and using tools to put all that into a multi-speaker podcast. See our new short course: "Building Live Voice Agents with Google’s ADK (Agent Development Kit),” taught by Google’s Lavi Nigam and Sita Lakshmi Sangameswaran. ADK provides modular components that make it easy to build and debug agents. It also includes a built-in web interface for tracing agentic reasoning. This course illustrates these concepts via building a live voice agent that can chain actions to complete a complex task like creating a podcast. This requires maintaining context, implementing guardrails, reasoning, and handling audio streaming, while keeping latency low. You’ll learn to: - Build voice agents that listen, reason, and respond - Guide your agent to follow a specific workflow to accomplish a task - Coordinate specialized agents to build an agentic podcast workflow that researches topics and produces multi-speaker audio - Understand how to deploy an agent into production Even if you’re not yet building voice systems, you'll find understanding how realtime agents stream data and maintain reliability useful for designing modern agentic applications. Please join here:
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Learn to build your own voice-activated AI assistant that can execute tasks like gathering recent AI news from the web, scripting out a podcast, and using tools to put all that into a multi-speaker podcast. See our new short course: "Building Live Voice Agents with Google’s ADK (Agent Development Kit),” taught by Google’s Lavi Nigam and Sita Lakshmi Sangameswaran. ADK provides modular components that make it easy to build and debug agents. It also includes a built-in web interface for tracing agentic reasoning. This course illustrates these concepts via building a live voice agent that can chain actions to complete a complex task like creating a podcast. This requires maintaining context, implementing guardrails, reasoning, and handling audio streaming, while keeping latency low. You’ll learn to: - Build voice agents that listen, reason, and respond - Guide your agent to follow a specific workflow to accomplish a task - Coordinate specialized agents to build an agentic podcast workflow that researches topics and produces multi-speaker audio - Understand how to deploy an agent into production Even if you’re not yet building voice systems, you'll find understanding how realtime agents stream data and maintain reliability useful for designing modern agentic applications. Please join here:

Andrew Ng

117,717 просмотров • 8 месяцев назад

New Agentic AI short course! AI Agentic Design Patterns with AutoGen, taught by Microsoft Research's Chi Wang and Penn State's Qingyun Wu (Hiring), shows you how to use AutoGen to implement agentic design patterns like multi-agent collaboration, sequential and nested chat, reflection, tool use, and planning. Learn how to build and combine multiple specialized agents – such as researchers, planners, coders, writers, and critics – that interact to execute complex workflows, like generating detailed financial reports, that would otherwise have taken extensive manual effort. This course illustrates key agentic design principles with many fun demonstrations. For example, you'll build a conversational chess game using two player agents, each of which can use a tool to validate moves and update the board state, while engaging in lively banter about the game! I've enjoyed using AutoGen, and think you will too. Sign up to get started here:
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New Agentic AI short course! AI Agentic Design Patterns with AutoGen, taught by Microsoft Research's Chi Wang and Penn State's Qingyun Wu (Hiring), shows you how to use AutoGen to implement agentic design patterns like multi-agent collaboration, sequential and nested chat, reflection, tool use, and planning. Learn how to build and combine multiple specialized agents – such as researchers, planners, coders, writers, and critics – that interact to execute complex workflows, like generating detailed financial reports, that would otherwise have taken extensive manual effort. This course illustrates key agentic design principles with many fun demonstrations. For example, you'll build a conversational chess game using two player agents, each of which can use a tool to validate moves and update the board state, while engaging in lively banter about the game! I've enjoyed using AutoGen, and think you will too. Sign up to get started here:

Andrew Ng

218,359 просмотров • 2 лет назад

The best way to learn AI is to build with agents. To help with that, we've launched hands-on labs and a new series on Agentic Engineering. First topic: Agent Skills. Next in the pipeline: planning, context engineering, multi-agent systems, long-running agents,.. Go build!
9:09

The best way to learn AI is to build with agents. To help with that, we've launched hands-on labs and a new series on Agentic Engineering. First topic: Agent Skills. Next in the pipeline: planning, context engineering, multi-agent systems, long-running agents,.. Go build!

elvis

31,802 просмотров • 1 месяц назад