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How Raspberry Pi Can Be Used For ML Applications

How Raspberry Pi Can Be Used For ML Applications

How Raspberry Pi Can Be Used For ML Applications

How Raspberry Pi Can Be Used For ML Applications

Smart ways to reach out to technology and Raspberry Pi have the greatest connection. There is no doubt that experimentation with the smart computer will take you to places. With Raspberry Pi, we are sure you can accomplish a lot more than you think. 


People may add voice, audio edge processing, and machine learning capabilities to the internet of things (IoT) applications using one of the many Raspberry Pi kits that are readily accessible. 


However, before jumping directly to the applications, let’s learn a little more about Raspberry Pi and Machine Learning. 

What is Raspberry Pi?

The Raspberry Pi is a small, reasonably priced computer that comes in the size of a small card, connects to a computer monitor or TV, and can be used with a regular keyboard and mouse. It is a competent tiny gadget that enables users of all ages to understand computing and learn how to develop in languages like Python and Scratch.


It is equipped with USB ports, an ARM quad-core processor, an HDMI connector, an Ethernet jack, a micro SD card slot, and other elements found on a computer's motherboard. Just because of that, it qualifies as an IoT. Not every item is a Raspberry Pi. Similar boards include the Arduino, ASUS Tinker Board S, and NVIDIA Jetson Nano Developer Kit.


Raspberry Pi may interface with the outside environment in various digital project manufacturers, such as music producers, parent detectors, weather stations, and tweeting birdhouses with infrared cameras.


What is Machine Learning?

The study of artificial intelligence led to the development of the field of computer science known as "machine learning." It can be described as the study and application of teachable algorithms. 


One of the Machine algorithms works with data sets where each data item has already been categorized. Simply put, users provide the algorithm instructions on what values to look for and what responses are reasonable. The model is then tested using fresh data that wasn't used in training to see if it can make accurate predictions based on prior experience with the data.


How does Raspberry Pi's incorporation with AI or ML work?

Let's first review what Raspberry Pi is and its history of applications. In a nutshell, the Raspberry Pi is probably a tiny, credit card-sized computer that costs just $35. 


Although the Raspberry Pi can be used as a standard, all-purpose computer, most people utilize it as the foundation for their hardware projects.


These ideas range from straightforward toys to intricate devices carrying crucial tasks. 


Machine learning has recently become available in a wide range of fields, and this trend is expected to continue for the foreseeable future. 


Anyone with a moderately strong desktop computer can now exploit technology that was previously only accessible to supercomputer operators. However, Microsoft is currently promoting the development of machine learning for embedded systems so that the reduction won't end there.


Applications might be as straightforward as your TV's media center or as complicated as a home automation system. Do keep in mind that while this type of project may always be done using desktop computers, doing so would not be very practical given the Raspberry Pi's inexpensive cost and compact size.


  • Benefits of ML with the Raspberry Pi

The Embedded Learning Library (ELL) is a collection of tools that enables smaller, less powerful devices like Arduinos, Raspberry Pis, and analogous models to benefit from machine learning. Microsoft created this library intending to make it helpful to everyone. 


There are implementation examples for computer vision, audio keyword recognition, and other domains. The library can, however, be expanded to any application where a small embedded system might profit from machine learning. It is not just confined to these example applications.


However, there is a slight delay when using your Raspberry Pi to run a machine learning algorithm. Large CPU loads typically cause small SoCs to overheat. However, installing a heatsink and fan has surely been observed before. Don't let the lack of a supercomputer prevent you from researching machine learning if you think it might be useful. You can always create a cluster to finish your operation a little bit quicker if you need more power than one Raspberry Pi can supply.


  • Using the Raspberry Pi for ML projects

It is hard to judge what may or may not work. However, TensorFlow Lite suggests that there is a scope to build different applications with it. 


TensorFlow Lite running on a Raspberry Pi 4 can achieve performance on par with NVIDIA's Jetson Nano at a small fraction of the price and power expenditure. By including a Coral Edge TPU USB Accelerator, you can obtain real-time performance with cutting-edge neural network topologies.


With this performance improvement, intriguing offline TensorFlow applications, including tracking and identifying moving objects, have now become possible.


Common uses of Applications of Raspberry Pi with ML

It is possible to construct a Raspberry Pi to photograph birds and identify whether or not they are swans. If the images are of swans, they are kept; otherwise, they are discarded.


The Raspberry Pi can also be used as a cheap web crawler to collect data for the model you're working on. This can be quite helpful and time-saving. We can carry out the crawling, data cleansing, and a variety of other operations on the Pi itself.


How about suggesting using a Raspberry Pi as a data hub to deliver sensor-related data to a server? It may be used in a variety of ways.


There are many examples of it, some of which are just as follows.


  • Quality Inspection

The most crucial characteristic of successful companies with a following of devoted customers is high-quality items. Customers are more concerned than ever with product quality and durability today. Artificial intelligence can be used with Raspberry Pi to restructure manufacturing processes more effectively.


Manufacturing companies can now inspect the volume, equipment failures, and flaws thanks to AI-powered computer vision and predictive analytics technology. Businesses may manage waste, overproduction, transportation, inventory, and other issues using predictive analytics enabled by machine learning (ML).


  • Video and Music Streaming

With cutting-edge business applications, the Raspberry Pi and AI's computer vision capabilities are breaking new ground. Businesses can enhance their facial and object recognition models by using a deep neural network on a Raspberry Pi.


Technologies for deep learning image recognition, like OpenCV and TensorFlow, extensively use Python modules and rich data sets. Due to the cost-effective integration of these ML technologies on Raspberry Pi, SMBs may increase production and efficiency.


  • Is Raspberry Pi good for ML?

Machine learning works better on Raspberry Pi. The causes are as follows. Since Linux can be run on the Raspberry Pi, a large variety of machine learning libraries and packages are available. GStreamer, FFmpeg, OpenCV, python, GCC, and others are a few examples.


  • Is Raspberry Pi 4 good for machine learning?

Fair comparisons of many different platforms remain difficult. The new Raspberry Pi 4 is unquestionably a trustworthy platform for edge machine learning inferencing, though if the details of the prior benchmarks have your attention.


  • Why is Raspberry Pi used in projects?

You may build a network monitoring solution using a Raspberry Pi that makes it simple to carry out all these operations. Although several Raspberry Pi projects are available, this one will allow you to gain knowledge of networking and network systems. While working on it, you'll get to put your understanding of computer networks to practice.


  • How is Raspberry Pi different from a desktop computer?

The Raspberry Pi and a standard computer differ significantly in terms of their design, dimensions, cost, connectivity, memory, and storage. Input/output elements, including ports, storage possibilities, screens, RAM, and construction/design, are a few other variations.


  • Is Raspberry Pi better than a laptop?

The Raspberry Pi and the small PC can be an alternative to the standard desktop computer. The decision essentially comes down to how sophisticated your computing requirements are. The Raspberry Pi has the smallest footprint and uses the least amount of power when doing standard computing tasks.


Bottom line 

When it comes to projects using machine learning, the Raspberry Pi is not a clear choice, and it is quite obvious. However, the board's small size and extremely low power consumption make it an affordable choice for prototypes, homemade devices, and other experimentations. These projects can use machine learning to increase their functionality and may be very useful for learning.


It is indisputable that the Raspberry Pi's inadequate processing power makes it nearly hard to train robust models on it. However, as was already mentioned, we may use the Pi to run previously trained models and allow them to conclude fresh data.


So, you may develop some innovative machine learning projects; you can push the capabilities of your Raspberry Pi. Moreover, to get your hands on original Raspberry PI kits, browse our selection of the best Raspberry Pi kits and individual products curated by our tech experts. As a Raspberry Pi manufacturer, you can be sure that you are getting only the best from Vilros.