Machine Learning is perhaps the most widely researched field today. It has gained such momentum in recent years that it has become an integral part of our everyday lives whether we know it or not. The recommendations that you get on Amazon Prime or Netflix? Machine Learning. Data analysis and predictions in healthcare? Machine Learning. Fraud detection systems used by Banks? Machine Learning again.
It is obvious that with the invention of new and more powerful algorithms, Machine Learning could do practically anything. To run such algorithms, you would need powerful hardware that can withstand the heavy computations. Naturally, the Raspberry Pi wouldn’t be the most obvious choice to run Machine Learning models due to its relatively ordinary specifications. Notwithstanding this caveat, it is possible to run basic models on the Raspberry Pi by making full use of the available hardware on the board. With advancements in the Pi itself, we can now perform basic image recognition and speech recognition among other applications.
One thing going for the Pi is that we can run pre-trained Machine Learning Models to perform certain tasks which will ease the computational burden and will also be efficient for smaller applications.
How can the Raspberry Pi be used for Machine Learning applications?
TensorFlow
Built by the Google Brain team, TensorFlow is widely used today for Machine Learning applications such as neural networks. It can be built using Python which is the way we program the Raspberry Pi.
To build from source for the Raspberry Pi, check out the official link
So what can we do with TensorFlow running on the Raspberry Pi?
- You can check if there are any available parking spaces in front of your home or workplace using basic object detection.
- Detect the presence of intruders in your house.
- A small robot that can autonomously navigate an obstacle course
- Self-driving vehicle prototypes
The applications are many and far-reaching. In spite of the limited RAM on the Pi itself, we can efficiently perform all of the above applications.
OpenCV
Open Source Computer Vision Library (or OpenCV) is a useful and powerful library that has many features to efficiently perform Face Recognition, Object recognition, text recognition and other Machine Learning applications.
The library is cross-platform and free for use under the open-source BSD license. OpenCV also supports some models from deep learning frameworks such as TensorFlow and PyTorch among others.
By using OpenCV we can make our Raspberry Pi do things like emotion recognition where the captured image is then classified as Happy, Sad, Angry and some other emotions. Gesture recognition, motion tracking, and other applications are also possible. We can create powerful robots that use Egomotion and Motion tracking to autonomously navigate through certain environments by using the captured images and classifying them as objects or markers that the robot must avoid.
OpenCV supports C++, Java, MATLAB and Python interfaces. Since Raspberry Pi is programmed using Python, we can directly install and use OpenCV with the Pi.
There are so many cool applications of Machine Learning that can be implemented on the Raspberry Pi. Michael Foale, an Astronaut, made a virtual co-pilot called Solar Pilot Guard, to prevent plane crashes. Check out the official Raspberry Pi blog post- Post
Conclusion
The Raspberry Pi is not an obvious choice when it comes to Machine Learning applications. However, the compact size of the board coupled with the fact that it consumes very less power makes it an inexpensive option for homemade gadgets and prototypes. Machine Learning can be implemented on these projects to boost their functionality.
One cannot deny the fact that it is almost impossible to train powerful models using the Raspberry Pi due to its limited processing power. However, as stated earlier, we can run already trained models on the Pi and enable it to infer something from the new data obtained.
So you can push the limits of your Raspberry Pi and come up with some cool Machine learning projects.
