Accelerate Deep Learning on Raspberry Pi [Video]
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 run 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 a 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. Let me help you get fast results. Enrol now, by clicking the button and let us show you how to develop Accelerated AI on Raspberry Pi.
All the code files are placed at https://github.com/PacktPublishing/-Accelerate-Deep-Learning-on-Raspberry-Pi-
Style and Approach
A complete course packed with step-by-step instructions, working examples, and helpful advice. This course is clearly divided into small parts that will help you understand each part individually and help you learn at your own pace.
|Course Length||1 hour 17 minutes|
|Date Of Publication||26 Feb 2019|
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