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Evaluating and Debugging Generative AI, created with wan deeee bee and taught by Carey Phelps, is now available! ML is an iterative process, and many of us used to track our models/experiments in a text file or spreadsheet, which gets messy. There’re now much better tools like Weights &... show more

Andrew Ng

1,579,221 subscribers

140,878 görüntüleme • 2 yıl önce •via X (Twitter)

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Akshay 🚀 profil fotoğrafı

Akshay 🚀2 yıl önce

@weights_biases @carey_phelps Big fan of the short courses that coursera is offering! Thank you! 🙏

Rufus profil fotoğrafı

Rufus2 yıl önce

@weights_biases @carey_phelps Another informative course big thanks to DeeplearningAI

Suman Das 👨🏽‍💻 ⚽ profil fotoğrafı

Suman Das 👨🏽‍💻 ⚽2 yıl önce

@weights_biases @carey_phelps Another gem.

Paul Ostergaard profil fotoğrafı

Paul Ostergaard2 yıl önce

@weights_biases @carey_phelps Love these short courses! Please keep ‘em coming!

crowdinsights profil fotoğrafı

crowdinsights2 yıl önce

@weights_biases @carey_phelps Huge fans of these courses! They're really informative, learned a lot. Thanks Andrew!🤟

Zenlytic profil fotoğrafı

Zenlytic2 yıl önce

@weights_biases @carey_phelps 🚀 Level up your ML game! Excited to announce 'Evaluating and Debugging Generative AI' with @weights_biases and @carey_phelps 🌟📊 #AIWorkshop #TechLearning

MnemicAI profil fotoğrafı

MnemicAI2 yıl önce

@weights_biases @carey_phelps Thank you - will sign up

Jan Kwapisz, Ph.D. profil fotoğrafı

Jan Kwapisz, Ph.D.2 yıl önce

@weights_biases @carey_phelps How does one can debug generative AI? Is there some secret souce?

Anthony O. Ekle profil fotoğrafı

Anthony O. Ekle2 yıl önce

@weights_biases @carey_phelps Another great course! Thanks to the DeepLearningAI team. I was wondering if there's an online course on "Graph Neural Networks or ML with Graph", or the possibility of one. Thank you! @AndrewYNg

AllThingsAI profil fotoğrafı

AllThingsAI2 yıl önce

@weights_biases @carey_phelps I extend my gratitude to DeeplearningAI for providing yet another enlightening course.

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My new course, Generative AI for Everyone, is now available! Learn how Generative AI works, how to use it in professional or personal settings, and how it will affect jobs, businesses and society. This course is accessible to everyone, and assumes no prior coding or AI experience. Please access it here:

My new course, Generative AI for Everyone, is now available! Learn how Generative AI works, how to use it in professional or personal settings, and how it will affect jobs, businesses and society. This course is accessible to everyone, and assumes no prior coding or AI experience. Please access it here:

Andrew Ng

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The GOV.UK AI Team is investigating how generative artificial intelligence and large language models could help the millions of people that use GOV.UK every week. Learn more about our #AI Experiments on our blog:

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GDS

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We're building Roblox to be a platform where anyone, anywhere, can be a creator. Generative AI is part of that vision: tools that help you bring your ideas to life seamlessly and interactively. More in our blog post:

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$🪐 WORLD CREATORS Generative AI opened the door for everyone to tell their stories without the need for a crew or a big budget. We started slowly with images and saw an enormous interest in tools like Midjourney, DALL-E, or Leonardo. Now, a bit more than a year later, we find ourselves in front of new technologies that are transforming our way of life, how we create, and how we consume content. ChatGPT is rapidly replacing Googling, and all the generative AI tools are replacing all the stock photos and videos, with tools like Runway enabling storytelling for everyone. And we won't take over Hollywood. We will surpass it in ways we can't even imagine right now. We'll be able to create worlds and stories beyond our wildest imagination. Generative AI is enabling creative people to go 1000x on what was possible before. At the core of everything we'll still need human creativity, and we have to understand that AI tools without it will be nothing. Until then, enjoy a short video I've put together to illustrate a small fraction of the worlds we will create soon. What worlds and stories are you excited to create?$

🪐 WORLD CREATORS Generative AI opened the door for everyone to tell their stories without the need for a crew or a big budget. We started slowly with images and saw an enormous interest in tools like Midjourney, DALL-E, or Leonardo. Now, a bit more than a year later, we find ourselves in front of new technologies that are transforming our way of life, how we create, and how we consume content. ChatGPT is rapidly replacing Googling, and all the generative AI tools are replacing all the stock photos and videos, with tools like Runway enabling storytelling for everyone. And we won't take over Hollywood. We will surpass it in ways we can't even imagine right now. We'll be able to create worlds and stories beyond our wildest imagination. Generative AI is enabling creative people to go 1000x on what was possible before. At the core of everything we'll still need human creativity, and we have to understand that AI tools without it will be nothing. Until then, enjoy a short video I've put together to illustrate a small fraction of the worlds we will create soon. What worlds and stories are you excited to create?

Alex Patrascu

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"AI evals force you into the shoes of your user." I love this clip from (Director at Arize) on why evaluations are the top AI skill to build in 2025. In my new interview, Aman breaks down: - How to write evals like a pro - 5 AI product principles he swears by - How to start practicing evals now Some quotes from Aman: "All PMs will be AI PMs. You'll either build on top of LLM APIs, use models in your backend, or use AI tools to solve problems in your daily work." "The waterfall process of writing a PRD and handing it to designers and engineers is dead. Your dev cycle needs to be much shorter." "Imagine showing up to a meeting with a prototype instead of a PRD. That's the shift PMs will need to make to communicate better in this new AI world." 📌 Watch now:

"AI evals force you into the shoes of your user." I love this clip from (Director at Arize) on why evaluations are the top AI skill to build in 2025. In my new interview, Aman breaks down: - How to write evals like a pro - 5 AI product principles he swears by - How to start practicing evals now Some quotes from Aman: "All PMs will be AI PMs. You'll either build on top of LLM APIs, use models in your backend, or use AI tools to solve problems in your daily work." "The waterfall process of writing a PRD and handing it to designers and engineers is dead. Your dev cycle needs to be much shorter." "Imagine showing up to a meeting with a prototype instead of a PRD. That's the shift PMs will need to make to communicate better in this new AI world." 📌 Watch now:

Peter Yang

15,973 görüntüleme • 1 yıl önce

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New course to bring you up to state-of-the-art at using AI to help you code: Build Apps with Windsurf's AI Coding Agents, built in partnership with WIndsurf (Codeium) and taught by Anshul Ramachandran! AI-assisted IDEs (Integrated Development Environments) make developers’ workflows faster, more efficient, and much more fun. Agentic tools like Windsurf are more than just code autocomplete—they are collaborative coding agents that help you break down complex applications, iterate efficiently, and generate code that spans multiple files. Although a lot of coding assistants share the same underlying large language models for planning and reasoning, a major point of distinction is how they handle tools, keep track of context, and stay aligned with your intent as a developer. For instance, if you make modifications to a class definition in your code and make the same modifications to other classes in the same directory, you might tell the AI agent "Do the same thing in similar places in this directory." Here, tracking your intent means understanding that “the same thing" refers to that recent edit you just made, which must be followed by appropriate search and tool-calling to implement the changes. In this course, you'll learn the inner workings of coding agents, their strengths and limitations, and how to use Windsurf to quickly build several applications. In detail, you'll: - Build a mental model of how agents work by combining human-action tracking, tool integration, and context awareness to carry out an agentic coding workflow. - Learn the challenges of code search and discovery and how a multi-step retrieval approach helps coding agents address them. - Use Windsurf to analyze and understand a large, old codebase and update it to the latest versions of the frameworks and packages it uses. - Build a Wikipedia data analysis app that retrieves, parses, and analyzes word frequencies. - Enhance the performance of your Wikipedia analysis app by adding caching, and through this, also learn how to course-correct when the AI agent produces unexpected results. - Learn tips and tricks such as keyboard shortcuts, autocomplete, and @ mentions to quickly call on agentic capabilities. - Use image/multimodal capabilities of the AI agent to increase your development velocity; you'll see an example of uploading a mockup with sketched-out UI features, and ask the agent to use that to build new functionality to an app. By the end of this course, you’ll understand agentic coding in-depth and know how to use it to make your development process much faster, more efficient, and enjoyable. Please sign up here!

Andrew Ng

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IBM

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Harness the latest generative AI models locally on your RTX AI PC with NVIDIA NIM Microservices and AI Blueprints. 🎨🖼️ Learn how NVIDIA NIM, @BFL_ML Flux, and ComfyUI empower creators to use 3D objects and simple text prompts to bring ideas to life.

Harness the latest generative AI models locally on your RTX AI PC with NVIDIA NIM Microservices and AI Blueprints. 🎨🖼️ Learn how NVIDIA NIM, @BFL_ML Flux, and ComfyUI empower creators to use 3D objects and simple text prompts to bring ideas to life.

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Today, generative AI can create a 3D race car. Roblox is now working on 4D generative AI, where that race car has an engine, moveable parts and a physics rig that enables it to drive down a track — all generated with just a text or voice prompt.

Today, generative AI can create a 3D race car. Roblox is now working on 4D generative AI, where that race car has an engine, moveable parts and a physics rig that enables it to drive down a track — all generated with just a text or voice prompt.

David Baszucki

467,358 görüntüleme • 2 yıl önce

Build and customize complex AI applications with a flexible framework in this new short course, Building AI Applications with Haystack. Created in collaboration with deepset, makers of Haystack, and taught by Tuana, who is the developer relations lead for Haystack at deepset. Generative AI technology is changing rapidly and it can be challenging to integrate APIs from different LLMs, vector databases, and various tools such as web search. In this course, you will learn how to use the Haystack framework to make your development process more modular, allowing you to manage complexity and focus more on building your application. In detail, you’ll: - Build a RAG pipeline using Haystack’s main building blocks – components, pipelines, and document stores. - Create custom components in your pipeline by building a Hacker News summarizer that extends your app’s ability to access APIs. - Use conditional routing to create a branching pipeline with a fallback to web search mechanism when the LLM does not have the necessary context to respond to the user's query. - Build a self-reflecting agent for named entity recognition that loops using an output validator custom component. - Create a chat agent using OpenAI's function-calling capabilities which allow you to provide Haystack pipelines as tools to the LLM, enhancing that agent's capabilities. By the end of this course, you will learn a high-level orchestration framework that can help make your applications flexible, extendible, and maintainable, even as the technology stack changes, new user needs arise, and you add new features to your application. Please sign up here:

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Studios like Ghostopia's Supersocial are already leveraging our Generative AI tools to empower development: seamlessly producing, experimenting with, and implementing Roblox-ready resources at the press of a button.

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Roblox

341,582 görüntüleme • 3 yıl önce

Our first Generative AI short course in JavaScript! GitHub recently reported that JavaScript is again the world’s most popular programming language. To support web developers exploring and developing with generative AI, we just launched a new short course in JavaScript taught by Jacob Lee, founding engineer at . In Build LLM Apps with LangChain.js you’ll learn elements common in AI development, including: (i) Using data loaders to pull data from common sources such as PDFs, websites, and databases (ii) Prompts, which are used to provide the LLM context (iii) Modules to support RAG such as text splitters and integrations with vector stores (iv) Working with different models to write applications that are not vendor-specific (v) Parsers, which extract and format the output for your downstream code to process You’ll also build with the LangChain Expression Language, which lets you easily compose sequences (also called chains) of modules to perform complex tasks using LLMs. Putting all this together, you’ll also work on a conversational question-answering LLM application capable of using external data as context. Please sign up here:

Our first Generative AI short course in JavaScript! GitHub recently reported that JavaScript is again the world’s most popular programming language. To support web developers exploring and developing with generative AI, we just launched a new short course in JavaScript taught by Jacob Lee, founding engineer at . In Build LLM Apps with LangChain.js you’ll learn elements common in AI development, including: (i) Using data loaders to pull data from common sources such as PDFs, websites, and databases (ii) Prompts, which are used to provide the LLM context (iii) Modules to support RAG such as text splitters and integrations with vector stores (iv) Working with different models to write applications that are not vendor-specific (v) Parsers, which extract and format the output for your downstream code to process You’ll also build with the LangChain Expression Language, which lets you easily compose sequences (also called chains) of modules to perform complex tasks using LLMs. Putting all this together, you’ll also work on a conversational question-answering LLM application capable of using external data as context. Please sign up here:

Andrew Ng

284,275 görüntüleme • 2 yıl önce

New course: MCP: Build Rich-Context AI Apps with Anthropic. Learn to build AI apps that access tools, data, and prompts using the Model Context Protocol in this short course, created in partnership with Anthropic Anthropic and taught by Elie Schoppik Elie Schoppik, its Head of Technical Education. Connecting AI applications to external systems that bring rich context to LLM-based applications has often meant writing custom integrations for each use case. MCP is an open protocol that standardizes how LLMs access tools, data, and prompts from external sources, and simplifies how you provide context to your LLM-based applications. For example, you can provide context via third-party tools that let your LLM make API calls to search the web, access data from local docs, retrieve code from a GitHub repo, and so on. MCP, developed by Anthropic, is based on a client-server architecture that defines the communication details between an MCP client, hosted inside the AI application, and an MCP server that exposes tools, resources, and prompt templates. The server can be a subprocess launched by the client that runs locally or an independent process running remotely. In this hands-on course, you'll learn the core architecture behind MCP. You’ll create an MCP-compatible chatbot, build and deploy an MCP server, and connect the chatbot to your MCP server and other open-source servers. Here’s what you’ll do: - Understand why MCP makes AI development less fragmented and standardizes connections between AI applications and external data sources - Learn the core components of the client-server architecture of MCP and the underlying communication mechanism - Build a chatbot with custom tools for searching academic papers, and transform it into an MCP-compatible application - Build a local MCP server that exposes tools, resources, and prompt templates using FastMCP, and test it using MCP Inspector - Create an MCP client inside your chatbot to dynamically connect to your server - Connect your chatbot to reference servers built by Anthropic’s MCP team, such as filesystem, which implements filesystem operations, and fetch, which extracts contents from the web as markdown - Configure Claude Desktop to connect to your server and others, and explore how it abstracts away the low-level logic of MCP clients - Deploy your MCP server remotely and test it with the Inspector or other MCP-compatible applications - Learn about the roadmap for future MCP development, such as multi-agent architecture, MCP registry API, server discovery, authorization, and authentication MCP is an exciting and important technology that lets you build rich-context AI applications that connect to a growing ecosystem of MCP servers, with minimal integration work. Please sign up here!

New course: MCP: Build Rich-Context AI Apps with Anthropic. Learn to build AI apps that access tools, data, and prompts using the Model Context Protocol in this short course, created in partnership with Anthropic Anthropic and taught by Elie Schoppik Elie Schoppik, its Head of Technical Education. Connecting AI applications to external systems that bring rich context to LLM-based applications has often meant writing custom integrations for each use case. MCP is an open protocol that standardizes how LLMs access tools, data, and prompts from external sources, and simplifies how you provide context to your LLM-based applications. For example, you can provide context via third-party tools that let your LLM make API calls to search the web, access data from local docs, retrieve code from a GitHub repo, and so on. MCP, developed by Anthropic, is based on a client-server architecture that defines the communication details between an MCP client, hosted inside the AI application, and an MCP server that exposes tools, resources, and prompt templates. The server can be a subprocess launched by the client that runs locally or an independent process running remotely. In this hands-on course, you'll learn the core architecture behind MCP. You’ll create an MCP-compatible chatbot, build and deploy an MCP server, and connect the chatbot to your MCP server and other open-source servers. Here’s what you’ll do: - Understand why MCP makes AI development less fragmented and standardizes connections between AI applications and external data sources - Learn the core components of the client-server architecture of MCP and the underlying communication mechanism - Build a chatbot with custom tools for searching academic papers, and transform it into an MCP-compatible application - Build a local MCP server that exposes tools, resources, and prompt templates using FastMCP, and test it using MCP Inspector - Create an MCP client inside your chatbot to dynamically connect to your server - Connect your chatbot to reference servers built by Anthropic’s MCP team, such as filesystem, which implements filesystem operations, and fetch, which extracts contents from the web as markdown - Configure Claude Desktop to connect to your server and others, and explore how it abstracts away the low-level logic of MCP clients - Deploy your MCP server remotely and test it with the Inspector or other MCP-compatible applications - Learn about the roadmap for future MCP development, such as multi-agent architecture, MCP registry API, server discovery, authorization, and authentication MCP is an exciting and important technology that lets you build rich-context AI applications that connect to a growing ecosystem of MCP servers, with minimal integration work. Please sign up here!

Andrew Ng

141,952 görüntüleme • 1 yıl önce

📺 New Tutorial: Submitting #AI Models to Learn how to easily upload your models to #Inferium and make them accessible to a wider audience. Our new video guide provides a step-by-step walkthrough of the submission process, covering everything from crafting informative descriptions to uploading your files. Watch now and contribute to the growing AI community! 👇

📺 New Tutorial: Submitting #AI Models to Learn how to easily upload your models to #Inferium and make them accessible to a wider audience. Our new video guide provides a step-by-step walkthrough of the submission process, covering everything from crafting informative descriptions to uploading your files. Watch now and contribute to the growing AI community! 👇

Inferium

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.Scott Wu to Joe Lonsdale on why Cognition uses AI in job interviews: "One or two years ago, people had this mentality of 'How do we make sure people aren't using AI while we're interviewing them?' And I think that has totally flipped. Honestly, I think that was wrong... If you're asking the question of like, how do we evaluate people on exactly the thing that AI can already do, that seems kind of the wrong. Our interview process has always been... you can use as much AI as you want to use. We're going to give you a few hours. Just build your whole own product surface. A lot of these are projects that, frankly, is like, if you were trying to do this by hand, you would not be able to get done in a few hours. So you kind of have to use AI for this. But in reality, what we actually want to test is, in addition to how familiar you are with these tools, what we actually want to test is, what do you think is the right thing to build? Or how do you make these product decisions? How do you make these trade-offs? How do you decide or collect information about what you should be doing?"

.Scott Wu to Joe Lonsdale on why Cognition uses AI in job interviews: "One or two years ago, people had this mentality of 'How do we make sure people aren't using AI while we're interviewing them?' And I think that has totally flipped. Honestly, I think that was wrong... If you're asking the question of like, how do we evaluate people on exactly the thing that AI can already do, that seems kind of the wrong. Our interview process has always been... you can use as much AI as you want to use. We're going to give you a few hours. Just build your whole own product surface. A lot of these are projects that, frankly, is like, if you were trying to do this by hand, you would not be able to get done in a few hours. So you kind of have to use AI for this. But in reality, what we actually want to test is, in addition to how familiar you are with these tools, what we actually want to test is, what do you think is the right thing to build? Or how do you make these product decisions? How do you make these trade-offs? How do you decide or collect information about what you should be doing?"

American Optimist

13,182 görüntüleme • 2 ay önce

.Runway's Gen-1, a video-to-video generative AI for generating new videos out of existing ones using text prompts and images, is now available on iOS devices. Learn more and download for free: #ai #generativeai #ios #video2video #runway #gen1

.Runway's Gen-1, a video-to-video generative AI for generating new videos out of existing ones using text prompts and images, is now available on iOS devices. Learn more and download for free: #ai #generativeai #ios #video2video #runway #gen1

80 LEVEL

41,890 görüntüleme • 3 yıl önce

This is Repo Prompt 2.0 A fully integrated agent, that makes it seamless to use RP's powerful MCP tools, with a built-in oracle and context builder. A first class experience showcasing how much better and efficient your agents can be with good context engineering tools.

This is Repo Prompt 2.0 A fully integrated agent, that makes it seamless to use RP's powerful MCP tools, with a built-in oracle and context builder. A first class experience showcasing how much better and efficient your agents can be with good context engineering tools.

eric provencher

38,921 görüntüleme • 3 ay önce

Open models. Open data. Open tools. Jensen Huang explains how building AI in the open is key to trust and global progress. Learn more about our open models →

Open models. Open data. Open tools. Jensen Huang explains how building AI in the open is key to trust and global progress. Learn more about our open models →

NVIDIA AI

16,554 görüntüleme • 5 ay önce

Dara (CEO of Uber) on their AI spend: "We blew through our AI budget in a quarter, for the whole year. It is forcing us to adjust. We are going to meter headcount increases because to the extent that my engineers are getting much more efficient, their throughput is increasing. There's a cost to that, and it's a significant cost. AI adoption has been occurring in all parts of the business –– whether it's engineers and how they scope projects, how they build, debugging, platform migrations. I'm pushing the teams to fundamentally use the power of AI to rebuild systems and processes from the bottoms up. I do think it's a combination for us right now of encouraging adoption, but then driving efficiency. We're using the more expensive models to explore. Once we scale some of these experiences, we'll look to bring in more efficient models that are more efficient on a token basis or are open source."

Dara (CEO of Uber) on their AI spend: "We blew through our AI budget in a quarter, for the whole year. It is forcing us to adjust. We are going to meter headcount increases because to the extent that my engineers are getting much more efficient, their throughput is increasing. There's a cost to that, and it's a significant cost. AI adoption has been occurring in all parts of the business –– whether it's engineers and how they scope projects, how they build, debugging, platform migrations. I'm pushing the teams to fundamentally use the power of AI to rebuild systems and processes from the bottoms up. I do think it's a combination for us right now of encouraging adoption, but then driving efficiency. We're using the more expensive models to explore. Once we scale some of these experiences, we'll look to bring in more efficient models that are more efficient on a token basis or are open source."

Patrick OShaughnessy

614,600 görüntüleme • 19 gün önce