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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 Build Long-Context AI Apps with Jamba, built in partnership with AI21 Labs. The transformer architecture is the foundation of most large language models, but they're not efficient at processing long context lengths. There's an alternative to transformers called Mamba that could process very long input contexts by reading an arbitrary long context and compressing it to a fixed-size representation. A lot of people have been excited about Mamba as a possible alternative or successor to the transformer, but researchers found that the pure Mamba architecture underperforms when the context is very long, since a compression mechanism causes it to lose information. AI21 developed a novel Jamba model that combines a traditional transformer with Mamba to take advantage of Mamba's efficiency, and also the transformer's attention mechanism to how retrieve the right piece of information at the right time. In detail, one of the strengths of the transformer is that it compares every pair of input tokens to see how related to each other. This is the attention mechanism that the decider would say when processing one word, what other words in a sentence they should be paying attention to. But this is quadratic cost in the input length. And while there are various techniques to bring down this cost, it has still been expensive because transformers can handle very long input context length. Then the paper "Mamba: Linear-Time Sequence Modeling with Selective State Spaces" by Albert Gu and Tri Dao describe a modern state space model to perform as well and can be efficiently implemented on GPUs. But Mamba was not as good as a transformer at long-distance relationships and in-context learning. Then the Jamba model, which is a hybrid transformer Mamba architecture, tries to capture the best of both worlds. I'm delighted the instructors for this course are Chen Wang, who is a lead Solution architect and Chen Almagor, who is an algorithm tech Lead from AI21 Labs. Both experts in Mamba and Jamba and Transformers. We're excited to be here and teach a course on Jamba. In the course, we will explore the underlying structure of the Jamba model, including transformers, but focusing on the less well-known state based aspects of the model and understand why this hybrid architecture is beneficial. We will also learn about strategies for expanding the context length of language models, review key aspects in evaluating long context models, and understand the advantages of Jamba in these settings. You will also get hands-on experience using Jamba with labs on prompting processing documents, tool calling, and RAG applications, all with a focus on the advantage of long context window of the Jamba model to improve performance. Many people have worked to create this course. I'd like to thank Ian Cox from AI21 labs and from DeepLearning.AI, Esmaeil Gargari and and Geoff Ladwig. The first lesson will be an overview of the Jamba model. That sounds great. Let's go on to the next video and learn about Jamba.
course detail
Next Lesson
Week 1: Build Long-Context AI Apps with Jamba
  • Introduction
    Video
    ใƒป
    3 mins
  • Overview
    Video
    ใƒป
    5 mins
  • Transformer-Mamba Hybrid LLM Architecture
    Video
    ใƒป
    14 mins
  • Jamba Prompting and Documents
    Video with Code Example
    ใƒป
    8 mins
  • Tool Calling
    Video with Code Example
    ใƒป
    6 mins
  • Expand the Context Window Size
    Video
    ใƒป
    13 mins
  • Long Context Prompting
    Video with Code Example
    ใƒป
    3 mins
  • Conversational RAG
    Video with Code Example
    ใƒป
    8 mins
  • Conclusion
    Video
    ใƒป
    1 min
  • Course Feedback
  • Community