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๐Ÿ’ป ย  Accessing Utils File and Helper Functions

In each notebook on the top menu:

1: ย  Click on "File"

2: ย  Then, click on "Open"

You will be able to see all the notebook files for the lesson, including any helper functions used in the notebook on the left sidebar. See the following image for the steps above.


๐Ÿ’ป ย  Downloading Notebooks

In each notebook on the top menu:

1: ย  Click on "File"

2: ย  Then, click on "Download as"

3: ย  Then, click on "Notebook (.ipynb)"


๐Ÿ’ป ย  Uploading Your Files

After following the steps shown in the previous section ("File" => "Open"), then click on "Upload" button to upload your files.


๐Ÿ“— ย  See Your Progress

Once you enroll in this courseโ€”or any other short course on the DeepLearning.AI platformโ€”and open it, you can click on 'My Learning' at the top right corner of the desktop view. There, you will be able to see all the short courses you have enrolled in and your progress in each one.

Additionally, your progress in each short course is displayed at the bottom-left corner of the learning page for each course (desktop view).


๐Ÿ“ฑ ย  Features to Use

๐ŸŽž ย  Adjust Video Speed: Click on the gear icon (โš™) on the video and then from the Speed option, choose your desired video speed.

๐Ÿ—ฃ ย  Captions (English and Spanish): Click on the gear icon (โš™) on the video and then from the Captions option, choose to see the captions either in English or Spanish.

๐Ÿ”… ย  Video Quality: If you do not have access to high-speed internet, click on the gear icon (โš™) on the video and then from Quality, choose the quality that works the best for your Internet speed.

๐Ÿ–ฅ ย  Picture in Picture (PiP): This feature allows you to continue watching the video when you switch to another browser tab or window. Click on the small rectangle shape on the video to go to PiP mode.

โˆš ย  Hide and Unhide Lesson Navigation Menu: If you do not have a large screen, you may click on the small hamburger icon beside the title of the course to hide the left-side navigation menu. You can then unhide it by clicking on the same icon again.


๐Ÿง‘ ย  Efficient Learning Tips

The following tips can help you have an efficient learning experience with this short course and other courses.

๐Ÿง‘ ย  Create a Dedicated Study Space: Establish a quiet, organized workspace free from distractions. A dedicated learning environment can significantly improve concentration and overall learning efficiency.

๐Ÿ“… ย  Develop a Consistent Learning Schedule: Consistency is key to learning. Set out specific times in your day for study and make it a routine. Consistent study times help build a habit and improve information retention.

Tip: Set a recurring event and reminder in your calendar, with clear action items, to get regular notifications about your study plans and goals.

โ˜• ย  Take Regular Breaks: Include short breaks in your study sessions. The Pomodoro Technique, which involves studying for 25 minutes followed by a 5-minute break, can be particularly effective.

๐Ÿ’ฌ ย  Engage with the Community: Participate in forums, discussions, and group activities. Engaging with peers can provide additional insights, create a sense of community, and make learning more enjoyable.

โœ ย  Practice Active Learning: Don't just read or run notebooks or watch the material. Engage actively by taking notes, summarizing what you learn, teaching the concept to someone else, or applying the knowledge in your practical projects.


๐Ÿ“š ย  Enroll in Other Short Courses

Keep learning by enrolling in other short courses. We add new short courses regularly. Visit DeepLearning.AI Short Courses page to see our latest courses and begin learning new topics. ๐Ÿ‘‡

๐Ÿ‘‰๐Ÿ‘‰ ๐Ÿ”— DeepLearning.AI โ€“ All Short Courses [+]


๐Ÿ™‚ ย  Let Us Know What You Think

Your feedback helps us know what you liked and didn't like about the course. We read all your feedback and use them to improve this course and future courses. Please submit your feedback by clicking on "Course Feedback" option at the bottom of the lessons list menu (desktop view).

Also, you are more than welcome to join our community ๐Ÿ‘‰๐Ÿ‘‰ ๐Ÿ”— DeepLearning.AI Forum


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Welcome to Building Toward Computer Use with Anthropic. Built in partnership with Anthropic and taught by Colt Steele, whose Anthropic's Head of Curriculum. Welcome, Colt. Thanks, Andrew. I'm delighted to have the opportunity to share this course with all of you. Anthropic made a recent breakthrough and released a model that could use a computer. That is, it can look at the screen, a computer usually running in a virtual machine, take a screenshot and generate mouse clicks or keystrokes in sequence to execute some tasks, such as search the web using a browser and download an image, and so on. This computer use capability is built by using many features of large language models in combination, including their ability to process an image, such as to understand what's happening in a screenshot, or to use tools that generate mouse clicks and keystrokes. And these are wrapped in an iterative agent workflow to then carry out complex tasks by taking many actions on that computer. In this course, you learn about the individual features which will be useful for your applications even outside of LLM-based computer use, as well as see how we can all come together for computer use. And Colt will show you how all this works. Thanks, Andrew. In this course, you will learn how to use many of the models and features that all combine to enable computer use. So here's how the course will progress. You'll first learn a little bit about Anthropic's background and vision and what's unique about our family of models. Then we'll use the API to make some basic requests. This then leads to multi-modal requests, where you'll use the model to analyze images. Then you'll dive into prompting, which Anthropic has really leaned into making models much more predictable with solid prompting, you'll learn about the prompting tips that actually matter, things like chain of thought and n-shot prompting, as well as get a chance to use our prompt improver tools. Recently, large language models have been supporting large input contexts. Anthropic's Claude, for example, supports over 200,000 input tokens, which is more than 500 pages of text. Long inputs can be expensive to process, and that any long conversations with chatbot if you're processing that conversation history over and over to keep on generating that next response, that next response, then that too gets more expensive as that history gets longer as the conversation goes on. Exactly. And that brings us right to prompt caching. Prompt caching retains some of the results of processing prompts between invocation to the model, which can be a large cost and latency saver. You also get to use the model to generate calls to external tools and produce structured output, such as Json, and at the very end, we'll walk through a complete example of computer use that you can run on your own machine. Note that because of the nature of the tool, you will have to run that on a Docker image on your computer, rather than directly in the DeepLearning.AI notebook. I've tried out Computer use myself using Anthropic's models and found it really cool. And I think this capability will make possible a lot of new applications where you can build an AI assistant to use a computer to carry out tasks for you. Kind of think RPA or robotic process automation, which has been good at repetitive tasks but now easier to build and more general with LLM-based tools. Or as this technology is even better than even more flexible and more open-ended tasks. So gradually feel more and more like personal assistants. I could not agree more. Very excited to see where it goes. Many people have worked to create this course. I'd like to thank from Anthropic, Ben Mann, Maggie Vo, Kevin Garcia, the team working on computer use, and from DeepLearning.AI Geoff Ladwig and Esmaeil Gargari. Anthropic has built a lot of really great models, and I regularly use them myself. Colt will share details of these models in the next video. All right, let's get started.
course detail
Next Lesson
Week 1: Building toward Computer Use with Anthropic
  • Introduction
    Video
    ใƒป
    3 mins
  • Overview
    Video
    ใƒป
    7 mins
  • Working with the API
    Video with Code Example
    ใƒป
    15 mins
  • Multimodal Requests
    Video with Code Example
    ใƒป
    12 mins
  • Real World Prompting
    Video with Code Example
    ใƒป
    17 mins
  • Prompt Caching
    Video with Code Example
    ใƒป
    12 mins
  • Tool Use
    Video with Code Example
    ใƒป
    17 mins
  • Computer Use
    Video
    ใƒป
    10 mins
  • Conclusion
    Video
    ใƒป
    1 min
  • Course Feedback
  • Community