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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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LLMs have demonstrated an amazing ability to interact with humans using natural language, which has opened the door to many new applications. But how can LLMs also interact with our existing software infrastructure? For example, by letting it decide when to make a function call to a different program to get more information call to a different program to get more information or to take an action. LLMs were originally designed to generate text for humans, but some LLMs have now been trained to output formatted data, such as values stored as JSON, to make it easy to let the LLM decide when to call other code as a subroutine. This significantly expands what you can do with LMs, such as let them extract information from structured or tabular data which LMs have typically struggled with. Here to tell you more about this is Harrison Chase, co-founder and CEO of Landchain, who has also taught two previous short courses. Welcome back, Harrison. Thanks, Andrew. So great to be back. You're right, this new capability which OpenAI has named function Harrison, you've described Langchain in other courses, but maybe you could describe what has changed and what you'd be covering in this course. Absolutely. As you know, Langchain is an open source library that helps developers bridge the gap between traditional software and LLMs. It allows developers to support any number of different LLMs and provides over 500 integrations to different language models, vector stores, and tools, as well as supporting memory chains and agents. There are two significant changes that you will be getting into during the course. The second is changes to take advantage of the new function calling capability. You'll learn how to use that directly and we'll also show how it can be used to do tasks like tagging or extracting data. Function Calling makes building tools for LLMs simpler and more reliable. You'll build some tools, and then, use them to build a conversational agent. You get to use all of those elements in the course and in the final project. That sounds great, Harrison. And I think, Many people have worked to make this course possible. We're grateful to Lance Martin and Nuno Kampas from Landchain. And on the deeplearning.ai side, Jeff Lodwick and Eshmel Gagari also contributed to this course.
course detail
Next Lesson
Functions, Tools and Agents with LangChain
  • Introduction
    Video
    ใƒป
    2 mins
  • OpenAI Function Calling
    Video with Code Example
    ใƒป
    13 mins
  • LangChain Expression Language (LCEL)
    Video with Code Example
    ใƒป
    16 mins
  • OpenAI Function Calling in LangChain
    Video with Code Example
    ใƒป
    12 mins
  • Tagging and Extraction
    Video with Code Example
    ใƒป
    24 mins
  • Tools and Routing
    Video with Code Example
    ใƒป
    17 mins
  • Conversational Agent
    Video with Code Example
    ใƒป
    16 mins
  • Conclusion
    Video
    ใƒป
    1 min
  • Course Feedback
  • Community