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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‘‡

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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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retrieves relevant documents to give context to an LLM, and this makes it much better at answering queries and performing tasks. Many teams are using simple retrieval techniques based on semantic similarity or embeddings, but you learn more sophisticated techniques in this course, which will let you do much better than that. A common workflow in RAG is to take your query and embed that, then find the most similar documents, meaning ones with similar embeddings, and that's the context. But the problem with that is that it can tend to find documents that talk about similar topics as a query, but not actually contain the answer. But you can take the initial user query and rewrite. This is called query expansion. Rewrite it to pull in more directly related documents. Two key related techniques. One, to expand the optional query into multiple queries by rewording or rewriting it in different ways. And second, to even guess or hypothesize what the answer might look like to see if we can find anything in our document collection that looks more like an answer rather than only generally talking about the topics of the query. I'm delighted that your instructor for this course is Anton Troynikov. Anton has been one of the innovators driving forward the state-of-the-art and retrieval for AI applications. He is co-founder of Chroma, which provides one of the most popular open-source vector databases. If you've taken one of our LangChain short courses taught by Harrison Chase, you have very likely used Chroma. Thank you, Andrew. I'm really excited to be working with you on this course and share what I'm seeing out in the field in terms of what does and doesn't work in RAG deployments. We'll start off the course by doing a quick review of RAG applications. You will then learn about some of the pitfalls of retrieval where simple vector search doesn't do well. Then you'll learn several methods to improve the results. As Andrew mentioned, the first methods use an LLM to improve the query itself. Another method re-ranks query results with help from something called a cross encoder, which takes in a pair of sentences and produces a relevancy score. You'll also learn how to adapt the query embeddings based on user feedback to produce more relevant results. There's a lot of innovation going on in RAG right now. So in the final lesson, we'll also go over some of the cutting edge techniques that aren't mainstream yet and are only just now appearing in research. And I think they'll become much more mainstream soon. We'd like to acknowledge some of the folks that have worked on this course. From the Chroma team, we'd like to thank Jeff Huber, Hammad Bashir, Liquan Pei, and Ben Eggers, as well as Chroma's open-source developer community. From the Deep Learning team, we have Geoff Ladwig and Esmael Gargari. The first lesson starts with an overview of Rack. I hope you go on to watch that right after this. And with these techniques, it turns out it's possible for smaller teams than ever to build effective systems. So after this course, you might be able to build something really cool with an approach that previously would have been considered RAG tag.
course detail
Next Lesson
Week 1: Advanced Retrieval for AI with Chroma
  • Introduction
    Video
    ใƒป
    3 mins
  • Overview of embeddings-based retrieval
    Video with Code Example
    ใƒป
    12 mins
  • Pitfalls of retrieval - when simple vector search fails
    Video with Code Example
    ใƒป
    11 mins
  • Query Expansion
    Video with Code Example
    ใƒป
    9 mins
  • Cross-encoder re-ranking
    Video with Code Example
    ใƒป
    6 mins
  • Embedding adaptors
    Video with Code Example
    ใƒป
    8 mins
  • Other Techniques
    Video
    ใƒป
    1 min
  • Course Feedback
  • Community