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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 Introduction to On-Device AI, built a partnership with Qualcomm and taught by Krishna Sridhar. A modest smartphone may have 10 to 30 teraflops of compute power. When you take a picture, that smartphone may be running dozens of AI models simultaneously for real time semantic segmentation and scene understanding. In this course, you learn how to create AI applications that run on-device. These techniques are applicable not only to making your app potentially run on to about 7 billion smartphones out there, but also potentially billions of other devices, including cameras, robots, drones, VR headsets, and many more. Despite the differences in hardware and operating systems among all of these devices, the principles of the key technical steps for deploying on-device are actually quite similar for many of these devices. Given them although they've already trained, perhaps in the cloud, to deploy it on-device, the first step is model conversion. Which means converting your model from, say, a PyTorch or TensorFlow framework into a format compatible with the on-device runtime. In this step, the model is frozen into a neural network graph, which is then converted into an executable for the device. Devices such as smartphones, and edge devices often contain a mix of processing units, including CPUs, GPUs, and neural processing units or NPUs. Knowing the exact devices your Apple runs on allows optimizations they can dramatically enhance performance sometimes making all those run up to ten times faster. You'll also learn tools to help you accomplish this across many different devices. And this is important because there are a lot of different smartphone brands and models, with your mobile app potentially running on maybe over 300 different smartphone types. It's then also important to ensure that your model performs consistently across these many different devices. This might mean validating the on-device numerical correctness across a broad range of devices to prevent cases where a model operates correctly on one device, but not on another, due to hardware differences. You learn how to do all this. And then lastly, quantization is also a common step of running on-device models. As you see, in the real time segmentation app in this course, quantization can make your app run several times faster, while resulting much smaller model size. In our case, about four times faster with also four times smaller model size. Our instructor, Krishna Sridhar, is senior director of engineering at Qualcomm. He's been doing on-device AI for about a decade and has built critical deployment on-device infrastructure that might well be running on your smartphone right now. Krishna has directly helped deploy over a thousand models on devices and over 100,000 applications have used the tech he and his team have built. Thanks, Andrew. In this course, you'll first learn how to deploy an on-device model in order to reduce latency, improve privacy, as well as improve efficiency. You will deploy your first model on-device with just a few lines of code. The model will do real-time segmentation from your camera stream. You will learn four key concepts as part of this course. The first one is how to capture your model as a graph that can be portable and runnable on a device. The process of compilation of that graph for a specific device. The hardware acceleration of that model in order to run efficiently on-device, as well as the process of validating that particular model for numerical correctness on-device. Finally, you will learn how to quantize a model so you can improve the performance by nearly 4x while also reducing the footprint of that particular model. Finally, we will integrate this particular model in an Android application that you can play around with. Many people have learned to create this course. I'd like to thank from Qualcomm, Kory Watson, Gustav Larsson and Siddhika Nevrekar. Also Esmaeil Gargari, Geoff Ladwig from DeepLearning.AI also contributed to this course. On-device deployment of AI models is taking off and opens up a lot of exciting capabilities to build this API systems. Let's go on to the next video to get started.
course detail
Next Lesson
Introduction to on-device AI
  • Introduction
    Video
    ใƒป
    4 mins
  • Why on-device
    Video
    ใƒป
    5 mins
  • Deploying Segmentation Models On-Device
    Video with Code Example
    ใƒป
    15 mins
  • Preparing for on-device deployment
    Video with Code Example
    ใƒป
    14 mins
  • Quantizing Models
    Video with Code Example
    ใƒป
    13 mins
  • Device Integration
    Video
    ใƒป
    6 mins
  • Conclusion
    Video
    ใƒป
    1 min
  • Appendix - Building the App
    Code Example
    ใƒป
    10 mins
  • Course Feedback
  • Community