Transfer Learning

• Instead of training large networks we can transfer the learning from previously trained networks.
• By training only a small subset of layers we can use the previously trained model

Architecture

Advantages of Raspberry Pi

• Small and low power
• Moderate computational power
• It can be used to run small or medium-sized machine learning models on the edge

Edge Computing

• The new era of IoT(Internet of Things) the number of devices connected to the web is increasing by the million.
• The existence of such devices demands cloud-based services for data collection. But there is a higher need to get the data centers closer to the devices.

c

1. Train and Save a Model 
2. Save the same model, this time without any data-preprocessing or training operations
3. Transfer this saved model to the raspberry pi and compile it into a Movidius graph
4. Plug in the Neural Compute stick and perform inference on new data

Why Not Movidius?

• Does not have the right open sources resources
• Still in development
• Open Source Community of Intel's open source hardware lacks.
• Buggy in various aspects