How does image analysis actually work?
This section will introduce two of the most commonly used learning methods.
Mass learning method
Among deep learning methods, it is a method that requires a lot of data and network design, so it requires a lot of time and effort.
When applied technology is used or when a large amount of data/information is output, learning can be made more efficient.
Additionally, learning the network and designing it can take several days to a month, so it isn’t commonly used, but it is used when handling large amounts of data.
Transfer learning
Transfer learning is a method of learning that utilizes existing networks.
The advantage of existing networks is that they are significantly faster to learn than new networks even if some improvements will need to be made.
Transfer learning has the advantage of reducing data requirements significantly.
Utilizing a prepared organization is suggested as it works on grouping into things like “apples” and “oranges” rather than characterizing information without any preparation into innumerable classes.
Data can be reduced to 1/1000 using this method, resulting in a time saving and a reduction in the amount of data.
When training previously trained system models, transfer learning is used, while large-scale learning is done using the above-mentioned mass learning method.
Practical examples of deep learning
The purpose of this section is to introduce examples of how deep learning is used and what it can do.
Self-driving
“Self-driving” cars are a practical example of deep learning.
”Deep learning” enables machines to automate driving that was previously performed by humans by performing a large amount of human work efficiently. Also, you can.
In Japan, Toyota and different organizations have declared vehicles furnished with self-driving innovation, and deep learning innovation is effectively being executed.
Image inspection
“Image inspection” is a practical example of deep learning.
The function of image inspection involves taking pictures of objects and machines with a special camera, memorizing their characteristics, and comparing them to previously memorized characteristics of normal objects.
Security technology can also be used with image inspection by memorizing the characteristics of people in addition to detecting defects.
Image inspection requires deep learning to memorize large amounts of data and information in order to function properly.
Deep learning technology is used all around us, as well as in systems that remove defective products during food manufacturing, as well as in frozen food production.
Medical examination
By using AI and machines to examine abnormalities in the body such as cancer and inflammation, it is possible to efficiently and quickly detect abnormalities in the body using this technology.
It is anticipated that this strategy will keep on filling in ubiquity later on, and will be utilized as a countermeasure against the infection in numerous clinical settings all over the planet, including Japan.
Deep learning acceleration
Deep learning is accelerated by increasing the speed of “image analysis” and “data output,” as the name suggests.
The purpose of this section is to explain specific methods for “speeding up deep learning.”
Introduction of GPU
A graphics processing unit (GPU) is a semiconductor specialized in image processing that has been manufactured by semiconductor manufacturing companies around the world, such as NVIDIA of the United States.
However, hearing GPU does not reveal its true nature.
How does a GPU function?
This segment will make sense of this inquiry detail.
GPU are believed to be brains that specialize in image analysis and are processors that analyze and output images quickly and accurately.
The GPU is responsible for displaying all of the “text,” “images,” and “videos” you see on your computer.
When used in deep learning, GPUs can accelerate analysis and output of images much more quickly than before.
Compressing the model
A “model” that summarizes a PC’s or processor’s operations can also be compressed to speed up deep learning.
Data reduction and compression can significantly speed up learning by reducing the amount and size of data within the model.
Learners must spend a much greater amount of time learning compared to those who want to fly from Hokkaido to Tokyo or swim to Tokyo.
Cloud tuning
The cloud can be adjusted to speed up deep learning when multiple people are involved.
Additionally, the calculation speed of the cloud will be affected by whether it has a GPU.
By installing GPUs in the cloud, connecting multiple devices to one cloud, and sharing work, it is also possible to shorten work time.
Use MATLAB
Millions of engineers around the world use the numerical calculation platform MATLAB, developed by Math Works.
A number of features are available in MATLAB, including algorithm development, data visualization, graphical interfaces, and multilingual interface sharing.
Additionally, commands for deep learning can be found in industry, government offices, and educational institutions.
As a result, deep learning using transfer learning can be conducted even more quickly.
Conclusions
Currently, the world’s leading companies, like Nissan and Toyota, as well as other AI development companies in Japan, are implementing deep learning to support the world in the future. A lot of attention is being paid to deep learning, which is used by companies like IBM, Amazon, etc.
Deep learning automatically classifies and manages countless pieces of information, making it particularly useful for autonomous driving, speeding up PCs, and inspection of image data.
It was once thought that high-speed information processing would be impossible, but deep learning has made this possible thanks to its ability to process infinite amounts of information at high speeds.
Further speeding up image processing is possible with GPUs, which specialize in image processing.
It is important to note, however, that the foundations for such cutting-edge technology were laid in 1957, and as artificial intelligence grew, so did these technologies.
In designing these technologies, human brain circuits and visual circuits are used as inspiration, which later evolved into applied deep learning.
Engineers, researchers, and companies are likely to pay close attention to deep learning in the future. It is my hope that you have gained a better understanding of deep learning after reading this article to the end.
