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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 Evaluating and Debugging Generative AI. I'm here with Carey Phelps, founding product manager at Weights and Biases, and instructor for this short course. Hey Andrew, excited to be here. When you're building a machine learning system, keeping track of all the data, model, and hyperparameter options can get complicated. I've been on a lot of projects where I train a model, then tune the architecture, then retrain the model, and decide to change the training settings and so on. After iterating on the model a few times, you end up asking, you know, the model I trained last week, that worked pretty well, but how do I replicate that result from a week ago, and did I remember to save not just the hyperparameter values, but also the exact datasets I use? More generally, when running a lot of models, how do you systematically keep track of everything you're trying, and use the results you're seeing to efficiently drive improvements? Even for a small team, managing and tracking machine learning model training and evaluation gets complicated, and the complexity grows worse with larger teams. I've seen that many teams can be much more efficient if this step of machine learning development is done more rigorously. So, this short course covers tools and best practices for systematically tracking and debugging generative AI models during the development process. We'll be using tools from Weights and Biases which offers an easy and flexible set of tools that's become a bit of an industry standard for machine learning experiment tracking. Generative AI models will cover both large language models for text generation and diffusion models for image generation, but generative AI models adds an additional layer of complexity compared to supervised learning, given that their output is complex and so they can be harder to evaluate. So, Carey, you know these challenges really well. Can you share with learners what they'll be learning in this course? Yeah, absolutely. Thanks, Andrew. Hello, everyone. I'm excited to be here with you. In this course, we'll be focused on evaluating and debugging generative AI. First, we'll show you how to track and visualize your experiments. Then, we'll teach you how to monitor diffusion models. And we'll discuss how to evaluate and fine-tune LLMs. Throughout the course, you'll learn about a range of debugging and evaluation tools, including Experiments to track your machine learning experiments,. Artifacts to version and store datasets and models, Tables to visualize and examine predictions made by your models. Reports to collaborate and share experimental results, and the Model Registry to manage the lifecycle of your models. Finally, Prompts for evaluating large-language model generation. These tools can work with a wide range of frameworks and computing platforms, including Python, TensorFlow, or PyTorch. So, a lot of good stuff there. And quite a few people have contributed to the development of this course. On the Weights and Biases side, we're grateful to the hard work of Darek Kleczek, as well as Thomas Capelle, and from Deeplearning.ai, Geoff Ludwig and Tommy Nelson. By the end of this course, you understand best practices and also have a set of tools for systematically evaluating and debugging generative AI projects. I hope you enjoy the course.
course detail
Next Lesson
Week 1: Evaluating and Debugging Generative AI
  • Introduction
    Video
    ใƒป
    3 mins
  • Instrument W&B
    Video with Code Example
    ใƒป
    10 mins
  • Training a Diffusion Model with W&B
    Video with Code Example
    ใƒป
    5 mins
  • Evaluating Diffusion Models
    Video with Code Example
    ใƒป
    7 mins
  • LLM Evaluation and Tracing with W&B
    Video with Code Example
    ใƒป
    14 mins
  • Finetuning a language model
    Video with Code Example
    ใƒป
    7 mins
  • Conclusion
    Video
    ใƒป
    1 min
  • Course Feedback
  • Community