DeepLearning.AI
AI is the new electricity and will transform and improve nearly all areas of human lives.

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


Sign in

Create Your Account

Or, sign up with your email
Email Address

Already have an account? Sign in here!

By signing up, you agree to our Terms Of Use and Privacy Policy

Choose Your Learning Path

Enjoy 30% Off Now. Cancel Anytime!

MonthlyYearly

Change Your Plan

Your subscription plan will change at the end of your current billing period. Youโ€™ll continue to have access to your current plan until then.

View All Plans and Features

Welcome back!

Hi ,

We'd like to know you better so we can create more relevant courses. What do you do for work?

DeepLearning.AI
  • Explore Courses
  • Community
    • Forum
    • Events
    • Ambassadors
    • Ambassador Spotlight
  • My Learnings
  • daily streak fire

    You've achieved today's streak!

    Complete one lesson every day to keep the streak going.

    Su

    Mo

    Tu

    We

    Th

    Fr

    Sa

    free pass got

    You earned a Free Pass!

    Free Passes help protect your daily streak. Complete more lessons to earn up to 3 Free Passes.

    Free PassFree PassFree Pass
Hi and welcome to this short course, Understanding and Applying Text Embeddings with Vertex AI, built in partnership with Google Cloud. In this course, you'll learn about different properties and applications of text embeddings. We'll dive together into how to compute embeddings, that is feature vector representations of text sequences of arbitrary length, and we'll see how these sentence embeddings are a powerful tool for many applications like classification, outlier detection, and text clustering. If you've heard of word embedding algorithms like Word2Vec or GloVe, that just examine a single word at a time, this is a bit like that, but much more powerful, and much more general because it operates at the level of the meaning of a sentence or even a paragraph of text, and also works for sentences that contain words not seen in the training set. In this course, you'll also learn how to combine text generation capabilities of large language models with these sentence level embeddings and build a small-scale question answering system that answers questions about Python based on the database of Stack Overflow posts. I'd like to introduce the other instructor for this course, Nikita Namjushi. Thanks, Andrew. I'm so excited to be teaching this course with you. As part of my job at Google Cloud AI, I help developers build with large language models and I'm really looking forward to sharing practical tips that I've learned from working with many cloud customers and many many LLM applications. This course will consist of the following topics. In the first half, which I'll present, we'll first use an embeddings model to create and explore some text embeddings. Then we'll look together to go through a conceptual understanding of how these embeddings work and how embeddings for text sequences of arbitrary length are created and also use code to visualize different properties of embeddings. The second half is taught by Nikita. Well, after you've had a chance to explore some different properties of embeddings, you'll then see how to use them for classification, clustering, and outlier detection. Because sentence level embeddings start to get at the meaning of an entire sentence, this really helps an algorithm to reason more deeply and make better decisions about text. So, after this, we'll see how to use a text generation model and some of the different parameters you can adjust. And finally, we'll put everything you've learned about embeddings, semantic similarity, and text generation together to build a small-scale question-answering system. Many people have contributed to this course. We're grateful for Eva Liu and Carl Tanner from the Google Cloud team, and also on the DeepLearning.ai side, Daniel Vigilagra and Eddie Hsu. The first lesson will be about how to get started with embedding text. That sounds great. Let's get started.
course detail
Next Lesson
Week 1: Understanding and Applying Text Embeddings
  • Introduction
    Video
    ใƒป
    2 mins
  • Getting Started With Text Embeddings
    Video with Code Example
    ใƒป
    12 mins
  • Understanding Text Embeddings
    Video
    ใƒป
    8 mins
  • Visualizing Embeddings
    Video with Code Example
    ใƒป
    9 mins
  • Applications of Embeddings
    Video with Code Example
    ใƒป
    16 mins
  • Text Generation with Vertex AI
    Video with Code Example
    ใƒป
    15 mins
  • Building a Q&A System Using Semantic Search
    Video with Code Example
    ใƒป
    19 mins
  • Optional - Google Cloud Setup
    Code Example
    ใƒป
    10 mins
  • Conclusion
    Video
    ใƒป
    1 min
  • Course Feedback
  • Community