The emergence of Artificial Intelligence is one of the most anticipated developments of mankind. The progress and achievements that we have made are nothing short of revolutionary. From automation in industries to self-driving cars, they are helping us reach a higher state of existence. We even have Sophia, a social humanoid AI robot who received citizenship in Saudi Arabia in October 2017. The technology and community are growing by the day.
Amongst many practices and algorithms in the world of AI, Convolutional Neural Networks(CNN) has been persistent and proven itself to be a notable algorithm in the field of Computer Vision. When given proper training, this Deep Learning algorithm is capable of taking images as input and classifies them and recognizes the subject in those images. Consequently, the system is said to have a vision similar to ours.
The potential use cases of CNN are not limited to Computer Vision. The following are a few examples of its existing applications:
- Biometric Security Systems and Facial Recognition: The biometric security system is a system that provides high-level security authorization by recognizing the uniqueness of the physical characteristics of an individual. It is used in cellular devices, laptops, smart home systems, and several military security systems among many others.
Facial Recognition is one of the widely used biometric systems for achieving high-level security. The systems scan the face of the subject, map its uniqueness, and recognize the subject of interest. When other individuals are presented to the system for authorization, it could recognize the attempt of the breach and reject the request.
- Handwriting recognition: CNN is capable of recognizing handwritten letters and words to a minute extent. This can be used for comparison of handwriting in criminal proceedings, converting handwritten notes to text formats, etc.
- Object Recognition: Object recognition is self-explanatory. CNN is fed with tan image input and it can detect all the objects present in it. This may sound simple but the use cases of this function are very rewarding, to say the least. One of the practical use of object recognition is using it to recognize objects in an image for visually challenged users. It gives them perception which they would not be able to get otherwise.
It is also combined with other AI products like self-driving cars in order to detect objects in its surrounding.
If we go on there are several other use-cases, including colorization of black & white images, speech recognition (widely used in Alexa, Google Assistant, etc). These are only a few examples of the use cases of CNN. The extent of its application is only limited by what we make of it.
If you are excited by what we can achieve through AI and CNN, this is the session you would not want to miss. This Live Session will cover the fundamental aspects of CNN.
Session Speaker: We are extremely excited to have Dipanjan Sarkar, Data Science Lead at Applied Materials, and a Google Developer Expert in Machine Learning. He has delivered numerous expert sessions in the fields of Machine Learning, Deep Learning, Computer Vision, and Natural Language Processing. His contribution to the Data Science community has been nothing but impressive. He is among the Top 10 Data Scientists In India – 2020 by Analytics India Magazine.
This session is part of our Deep Learning Bootcamp. In case, you have not signed up for the bootcamp already, please fill in the form below.
This is going to be exciting. See you at the session 😊
Cheers & Happy Learning 🙂