Learn how an AI Assistant is built to use and accomplish tasks on computers.

Learn about Anthropic’s family of models, its approach to AI research, and the best way to prompt it, including multi-modal use cases. Learn about Anthropic’s family of models, its approach to AI research, and the best way to prompt it, including multi-modal use cases.Learn effective prompting techniques, implement prompt caching to reduce costs and latency, and build AI applications that can call tools. Learn effective prompting techniques, implement prompt caching to reduce costs and latency, and build AI applications that can call tools.See how you can combine multimodal capabilities, agentic workflows, and tool use to build an AI assistant that can navigate and interact with computer interfaces, executing tasks like web searches. See how you can combine multimodal capabilities, agentic workflows, and tool use to build an AI assistant that can navigate and interact with computer interfaces, executing tasks like web searches._Building Towards Computer Use with Anthropic introduces an innovative capability from Anthropic that enables models to interact with and navigate computer interfaces.Taught by Colt Steele, Anthropic’s Head of Curriculum, this course covers Anthropic’s family of models and the building blocks that lead to the amazing new application – Computer Use. Computer Use utilizes the capabilities of the latest models including image reasoning and tool use to enable an LLM-based agent to use a computer. Like a human user, the model processes an image of the screen, analyzes it to understand what’s going on, and navigates the computer by issuing mouse clicks and generating keyboard strokes to get things done.In this course, you’ll learn the features that lead up to computer use from working with the Anthropic’s API, to multimodal prompting, prompt caching, and tool use, ending in a demo that combines these features to build an AI assistant that uses a computer.In detail, you’ll:Learn Anthropic’s approach to AI research, principles of AI safety, alignment, and interpretability while understanding the key differences between its models.Make API requests to Claude, format messages for better responses, and control API parameters like system prompts, temperature, and max tokens for optimal responses.Write multi-modal prompts that combine text and image content blocks and build with streaming responses.Learn effective prompting techniques such as using prompt templates, structuring prompts in XML, and providing examples to get consistent high-quality responses.Learn to implement prompt caching and see how it can reduce costs and latency.Understand tool-use workflows and build a chatbot that can call different tools in response to users’ queries.See all these concepts come together in a demo that uses Anthropic Computer Use to achieve a task on a computer.Start utilizing Anthropic’s family of models to build towards Computer Use applications._Anyone who has basic Python knowledge, wants to learn how to use all of the features of the Anthropic family of models, and understand the capabilities of computer use applications. 

Building toward Computer Use with Anthropic

Learn MLOps tools for managing, versioning, debugging, and experimenting in your ML workflow.

Learn to evaluate programs utilizing LLMs as well as generative image models using platform-independent tools Learn to evaluate programs utilizing LLMs as well as generative image models using platform-independent toolsInstrument a training notebook, and add tracking, versioning, and logging Instrument a training notebook, and add tracking, versioning, and loggingImplement monitoring and tracing of LLMs over time in complex interactions Implement monitoring and tracing of LLMs over time in complex interactions_Machine learning and AI projects require managing diverse data sources, vast data volumes, model and parameter development, and conducting numerous test and evaluation experiments. Overseeing and tracking these aspects of a program can quickly become an overwhelming task.This course will introduce you to Machine Learning Operations tools that manage this workload. You will learn to use the Weights & Biases platform which makes it easy to track your experiments, run and version your data, and collaborate with your team.This course will teach you to:Instrument a Jupyter notebookManage hyperparameter configLog run metricsCollect artifacts for dataset and model versioningLog experiment resultsTrace prompts and responses to LLMs over time in complex interactionsWhen you complete this course, you will have a systematic workflow at your disposal to boost your productivity and accelerate your journey toward breakthrough results._Anyone who has familiarity with Python and PyTorch or similar framework and an interest in managing, versioning, and debugging their machine learning workflow.

Evaluating and Debugging Generative AI

Learn how to easily build AI applications using open-source models and Hugging Face tools. Find and filter open-source models on Hugging Face Hub.

Find and filter open source models on Hugging Face Hub based on task, rankings, and memory requirements. Find and filter open source models on Hugging Face Hub based on task, rankings, and memory requirements.Write just a few lines of code using the transformers library to perform text, audio, image, and multimodal tasks. Write just a few lines of code using the transformers library to perform text, audio, image, and multimodal tasks.Easily share your AI apps with a user-friendly interface or via API and run them on the cloud using Gradio and Hugging Face Spaces. Easily share your AI apps with a user-friendly interface or via API and run them on the cloud using Gradio and Hugging Face Spaces._The availability of models and their weights for anyone to download enables a broader range of developers to innovate and create.In this course, you’ll select open source models from Hugging Face Hub to perform NLP, audio, image and multimodal tasks using the Hugging Face transformers library. Easily package your code into a user-friendly app that you can run on the cloud using Gradio and Hugging Face Spaces.You will:Use the transformers library to turn a small language model into a chatbot capable of multi-turn conversations to answer follow-up questions.Translate between languages, summarize documents, and measure the similarity between two pieces of text, which can be used for search and retrieval.Convert audio to text with Automatic Speech Recognition (ASR), and convert text to audio using Text to Speech (TTS).Perform zero-shot audio classification, to classify audio without fine-tuning the model.Generate an audio narration describing an image by combining object detection and text-to-speech models.  Identify objects or regions in an image by prompting a zero-shot image segmentation model with points to identify the object that you want to select.Implement visual question answering, image search, image captioning and other multimodal tasks.Share your AI app using Gradio and Hugging Face Spaces to run your applications in a user-friendly interface on the cloud or as an API. The course will provide you with the building blocks that you can combine into a pipeline to build your AI-enabled applications!_Anyone who wants to get started building AI applications quickly and easily using open source models.

Open Source Models with Hugging Face

Try out the features of the new Llama 3.2 models to build AI applications with multimodality.

Explore the features of the new Llama 3.2 model, from image classification, vision reasoning to tool use. Explore the features of the new Llama 3.2 model, from image classification, vision reasoning to tool use.Learn the details of Llama 3.2 prompting, tokenization, built-in and custom tool calling. Learn the details of Llama 3.2 prompting, tokenization, built-in and custom tool calling.Gain knowledge of the Llama stack, which is a standardized interface for building AI applications. Gain knowledge of the Llama stack, which is a standardized interface for building AI applications._Join our new short course, Introducing Multimodal Llama 3.2, and learn from Amit Sangani, Senior Director of AI Partner Engineering at Meta, to learn all about the latest additions to the Llama models 3.1 and 3.2, from custom tool calling to multimodality and the new Llama stack.Open models are a key building block of AI and a key enabler of AI research. With Meta’s family of open models, anyone can download, customize, fine-tune, or build new applications on top of them, allowing AI innovation. The Llama model family now ranges from 1B model parameters to its 405B foundation model, allowing for diverse use cases and applications.In this course, you’ll learn about the new vision capabilities that Llama 3.2 brings to the Llama family. You’ll learn how to leverage this along with tool-calling, and Llama Stack, which is an open-source orchestration layer for building on top of the Llama family of models.In detail, you’ll: Learn about the new models, how they were trained, their features, and how they fit into the Llama family.Understand how to do multimodal prompting with Llama and work on advanced image reasoning use cases such as understanding errors on a car dashboard, adding up the total of three restaurant receipts, grading written math homework, and many more.Learn different roles—system, user, assistant, ipython—in the Llama 3.1 and 3.2 family and the prompt format that identifies those roles.Understand how Llama uses the tiktoken tokenizer, and how it has expanded to a 128k vocabulary size that improves encoding efficiency and enables support for seven non-English languages.Learn how to prompt Llama to call both built-in and custom tools with examples for web search and solving math equations.Learn about ‘Llama Stack API’, which is a standardized interface for canonical toolchain components like fine-tuning or synthetic data generation to customize Llama models and build agentic applications.Start building exciting applications on Llama!_Anyone who has basic Python knowledge and wants to learn to quickly build on Llama and Llama stack.

Introducing Multimodal Llama 3.2

Learn to use OpenAI Canvas to write, code, and create more effectively in collaboration with AI.

Learn to write and code more effectively with OpenAI Canvas, using features like targeted editing, in-line feedback, changing the reading level, final polish, reviewing code, and fixing bugs. Learn to write and code more effectively with OpenAI Canvas, using features like targeted editing, in-line feedback, changing the reading level, final polish, reviewing code, and fixing bugs.Learn how to build practical use cases like creating a game app, generating Python code from plot screenshots, and creating SQL databases from architecture images. Learn how to build practical use cases like creating a game app, generating Python code from plot screenshots, and creating SQL databases from architecture images.Gain knowledge of how GPT-4o was trained to bring the OpenAI Canvas features and functionalities to life. Gain knowledge of how GPT-4o was trained to bring the OpenAI Canvas features and functionalities to life._Explore a new way to write and code with OpenAI Canvas, a user-friendly interface that allows you to brainstorm, draft, and refine text and code in collaboration with ChatGPT. In the short course Collaborative Writing and Coding with OpenAI Canvas, taught by Karina Nguyen, research lead at OpenAI, you’ll learn how to use this tool to enhance your writing and coding.Canvas provides a side-by-side workspace where you and ChatGPT can collaboratively edit and refine text or code. This makes brainstorming, drafting, and iterating on the text feel more natural and effective.For instance, after writing the first version of your code, Canvas can review it and give suggestions for improvement. It can also help with debugging by adding logs, identifying problems to fix, and writing comments for your code.This course will teach you how to make the most of the new interactive Canvas workspace and tools to make your writing more flexible, more efficient, and a lot more fun as you go behind the scenes with examples of the different use cases and learn what it takes to train the model to powers Canvas’s features and functionalities.In this video-only short course, you’ll:Learn how to ask for in-line feedback and control the iteration of your creative work by directly editing selected areas of your text or code from the model’s output.Learn how to access quick automation tools in a shortcut menu that allows you to modify your writing tone and length, enhance your code, and restore previous versions of your work.Learn how to use Canvas as a research assistant tool with an example of asking the model to reason through the screenshot of a plot to write a research report, in which you can ask questions within the report.Extend its use case by asking the model to write Python code to replicate the graph seen on a screenshot image.Go behind the scenes of how you can create a video game such as Space Battleship from scratch, edit it, and display it in one self-contained HTML file.Get a real-world application example of creating a SQL database from the image of its architecture.Understand the model training and design processes that power Canvas.By the end of this course, you’ll learn about all the possibilities that the new Canvas interface can bring to your writing and coding workflow._Anyone who wants to learn how to use the new OpenAI Canvas interface to write and code better.

Collaborative Writing and Coding with OpenAI Canvas

Learn how to use and prompt OpenAI's o1 model for complex reasoning tasks.

Learn about o1, what makes it work, how it performs, and the best scenarios to use it. Learn about o1, what makes it work, how it performs, and the best scenarios to use it.Learn how to prompt o1 effectively and when to delegate tasks to more cost-efficient, lower latency models. Learn how to prompt o1 effectively and when to delegate tasks to more cost-efficient, lower latency models.Learn how o1 outperforms on coding and vision reasoning tasks, and how to apply meta-prompting to optimize your applications. Learn how o1 outperforms on coding and vision reasoning tasks, and how to apply meta-prompting to optimize your applications._Learn how to effectively prompt and use OpenAI’s o1 model in Reasoning with o1, a short course built in collaboration with OpenAI and taught by Colin Jarvis, Head of AI Solutions at OpenAI.The o1 model is exceptionally good at abstract reasoning tasks. It has record-breaking performance on tasks such as planning, coding, analyzing, domain-specific reasoning like law, and other STEM subjects.In this course, you will learn how OpenAI utilized reinforcement learning to produce a model that uses ‘test-time compute’ to improve performance on many reasoning tasks. Learn what ‘chain-of-thought’ prompting is and how o1 autonomously utilizes it to break problems into smaller steps, try multiple strategies, and think through responses before returning them.In detail, you’ll: Learn to recognize what tasks the o1 model is suited for and when you might want to use a smaller or faster model or combine those two.Understand four key principles of prompting using o1 from “simple and direct” to “show rather than tell,” and explore the difference in performance.Implement a multi-step task in which o1 acts as an orchestrator creating a plan and handing it over to the 4o-mini model to execute the plan in sequence, balancing the trade-off between intelligence and cost.Use o1 for a coding task to build a new application, edit existing code, and test performance by running a coding competition between o1-mini and GPT 4o.Use o1 for image understanding and learn how it performs better with a hierarchy of reasoning, in which it incurs the latency and cost upfront, preprocessing the image and indexing it with rich details so it can be used for Q&A later.Learn a technique called meta-prompting, in which you use o1 to improve your prompts. Using a customer support evaluation set, you iteratively use o1 to modify a prompt to improve performance.Start building applications that require complex reasoning with o1._It’s helpful to be familiar with Python and have a basic understanding of LLM prompting and LLM application development.