Accelerate Deep Learning on Raspberry Pi

Accelerate Deep Learning on Raspberry Pi

Learn how we implemented Deep Learning Object Detection Models on Raspberry Pi and accelerated them with Intel Movidius Neural Compute Stick.

When we first got started in Deep Learning particularly in Computer Vision, we were really excited at the possibilities of this technology to help people. The only problem is, that image classification and object detection runs just fine on our expensive, power consuming and bulky Deep Learning machines. However, not everyone can afford or implement AI for their practical applications.

This is when we went searching for an affordable, compact, less power hungry alternative. Generally if we'd want to shrink our IoT and automation projects, we'd often look to the Raspberry Pi which is versatile computing solution for numerous problems. This made us ponder about how we can port out deep learning models to this compact computing unit. Not only that, but how could we run it at close to real-time?

Amongst the possible solutions we arrived at using the raspberry pi in conjunction with an AI Accelerator USB stick that was made by Intel to boost our object detection frame-rate. However it was not so simple to get it up and running. Implementing the documentation, we landed up with a series of bugs after bugs, which became a bit tedious.

After endless posts on forums, tutorials and blogs, we have documented a seamless guide in the form of this course; which will show you, step-by-step, on how to implement your own Deep Learning Object Detection models on video and webcam without all the wasteful debugging. So essentially, we've structured this training to reduce debugging, speed up your time to market and get you results sooner.

In this course, here's some of the things that you will learn:

• Getting Started with Raspberry Pi even if you are a beginner,

• Deep Learning Basics,

• Object Detection Models - Pros and Cons of each CNN,

• Setup and Install Movidius Neural Compute Stick (NCS) SDK,
  Currently, the OpenVINO is available for Raspbian, so the NCS2 is already compatible with the Raspberry Pi, but this course is mainly for the Movidius (NCS version 1).

• Run Yolo and Mobilenet SSD object detection models in recorded or live video

You also get helpful bonuses:

*OpenCV CPU inference

*Introduction to Custom Model Training

Personal help within the course

I donate my time to regularly hold office hours with students. During the office hours you can ask me any business question you want, and I will do my best to help you. The office hours are free. I don't try to sell anything.

Students can start discussions and message me with private questions. I answer 99% of questions within 24 hours. I love helping students who take my courses and I look forward to helping you. 

I regularly update this course to reflect the current marketing landscape.

Get a Career Boost with a Certificate of Completion  

Upon completing 100% of this course, you will be emailed a certificate of completion. You can show it as proof of your expertise and that you have completed a certain number of hours of instruction.

If you want to get a marketing job or freelancing clients, a certificate from this course can help you appear as a stronger candidate for Artificial Intelligence jobs.

Money-Back Guarantee

The course comes with an unconditional, Udemy-backed, 30-day money-back guarantee. This is not just a guarantee, it's my personal promise to you that I will go out of my way to help you succeed just like I've done for thousands of my other students. 

Let me help you get fast results.  Enroll now, by clicking the button and let us show you how to develop Accelerated AI on Raspberry Pi.

How to Accelerate your AI Object Detection Models 5X faster on a Raspberry Pi 3, using Intel Movidius for Deep Learning

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What you will learn

• Learn how to get Started with Raspberry Pi from Scratch
• Discover various Object Detection models
• Introduction to Deep Learning and Tensorflow lite

Rating: 3.5

Level: Beginner Level

Duration: 2 hours

Instructor: Augmented Startups