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Quick Guide & Tips

đŸ’»   Accessing Utils File and Helper Functions

In each notebook on the top menu:

1:   Click on "File"

2:   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.

🔄   Reset User Workspace

If you need to reset your workspace to its original state, follow these quick steps:

1:   Access the Menu: Look for the three-dot menu (⋼) in the top-right corner of the notebook toolbar.

2:   Restore Original Version: Click on "Restore Original Version" from the dropdown menu.

For more detailed instructions, please visit our Reset Workspace Guide.

đŸ’»   Downloading Notebooks

In each notebook on the top menu:

1:   Click on "File"

2:   Then, click on "Download as"

3:   Then, click on "Notebook (.ipynb)"

đŸ’»   Uploading Your Files

After following the steps shown in the previous section ("File" => "Open"), then click on "Upload" button to upload your files.

📗   See Your Progress

Once you enroll in this course—or any other short course on the DeepLearning.AI platform—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).

đŸ“±   Features to Use

🎞   Adjust Video Speed: Click on the gear icon (⚙) on the video and then from the Speed option, choose your desired video speed.

🗣   Captions (English and Spanish): Click on the gear icon (⚙) on the video and then from the Captions option, choose to see the captions either in English or Spanish.

🔅   Video Quality: If you do not have access to high-speed internet, click on the gear icon (⚙) on the video and then from Quality, choose the quality that works the best for your Internet speed.

đŸ–„   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.

√   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.

🧑   Efficient Learning Tips

The following tips can help you have an efficient learning experience with this short course and other courses.

🧑   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.

📅   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.

☕   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.

💬   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.

✍   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.

📚   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. 👇

👉👉 🔗 DeepLearning.AI – All Short Courses [+]

🙂   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 👉👉 🔗 DeepLearning.AI Forum

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DeepLearning.AI
CourseIntermediate3h7m

Transformers in Practice

Instructor: Sharon Zhou

AMD logo

Earn a certificate with PRO

Transformers in Practice

  • Intermediate
  • 3h7m
  • 19 Video Lessons
  • 8 Code Examples
  • 6 Graded AssignmentsPRO
  • Earn a certificate withPRO
  • Instructor: Sharon Zhou
  • AMD
    AMD
  • Learn more about Membership PRO Plan

Understand what's actually happening inside your LLMs

  • Understand text generation: see how transformers produce output one token at a time, and why that explains so much about their behavior.

  • Look inside the model: build intuition for what attention is really doing, how positional encoding works, and how layers combine to make predictions.

  • Optimize for production: learn how quantization, KV caching, and flash attention help transformers run efficiently on GPUs.

Why Enroll

If you’ve worked with LLMs, you’ve probably run into slow inference, out-of-memory errors, or hallucinations you couldn’t explain. There’s no shortage of resources on how transformers work, but most of them either ask you to build one from scratch or get lost in theory that doesn’t connect to the problems you’re actually facing.

Transformers in Practice is different. Taught by Sharon Zhou, VP of Engineering & AI at AMD, this course gives you a complete practical view of how transformers work, from how they generate text to what’s happening inside the model to how it all gets optimized to run on real hardware. Interactive visualizations throughout let you see key concepts in action and build intuition that actually sticks.

Here’s what you’ll learn:

  • Model Behavior: You’ll learn how LLMs generate text through an autoregressive loop, selecting one token at a time from a probability distribution. You’ll see how sampling parameters like temperature shape the output, why hallucinations happen, and how techniques like RAG, constrained generation, and chain-of-thought reasoning all work within this same loop.
  • Model Architecture and Attention: You’ll look inside the transformer to understand what attention is really doing, how positional encoding tracks token order, and how multiple layers and attention heads work together to turn an input sequence into a next-token prediction.
  • Scaling and Deploying: You’ll learn why GPUs are well-suited for transformer inference and where the real bottlenecks are. You’ll build practical intuition for quantization, KV caching, flash attention, and speculative decoding, including the tradeoffs each one introduces for cost, speed, and output quality.

You’ll earn a certificate upon completing the course, recognizing your skills in transformer-based language models.

In partnership with

null

We built this course with AMD to help engineers move beyond treating LLMs as black boxes. You’ll build practical intuition for how transformers generate text, process context, and run efficiently on GPUs, while learning techniques and concepts that apply across transformer-based models and hardware environments.

Who should join?

This course is designed for software engineers, ML engineers, and developers who work with LLMs and want to understand what’s actually happening under the hood.

You don’t need to have built a model from scratch, but you should be comfortable using LLMs through an API or chat interface and have a basic understanding of neural network concepts like weights, layers, and training.

Course Outline

Transformers in Practice

Module 1: Observed behavior
Module 2: LLM internals and attention
Module 3: Scaling and deploying

Instructor

Sharon Zhou

Sharon Zhou

VP of Engineering & AI at AMD

What Learners From Previous Courses Say About DeepLearning.AI

Jan Zawadzki

“Within a few minutes and a couple slides, I had the feeling that I could learn any concept. I felt like a superhero after this course. I didn’t know much about deep learning before, but I felt like I gained a strong foothold afterward.”

Kritika Jalan

“The whole specialization was like a one-stop-shop for me to decode neural networks and understand the math and logic behind every variation of it. I can say neural networks are less of a black box for a lot of us after taking the course.”

Chris Morrow – Deep Learning Specialization

“During my Amazon interview, I was able to describe, in detail, how a prediction model works, how to select the data, how to train the model, and the use cases in which this model could add value to the customer.”

Learner Reviews

Frequently Asked Questions

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How much does a Pro membership cost?

The DeepLearning.AI Pro membership costs $25/mo billed annually and $30/mo billed monthly.

More pricing details are available on the membership page.

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Will I receive a certificate at the end of the course?

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