The significance of CNNs in Machine Learning
CNNs (and deep learning in general) are so important in machine learning because they are capable of learning features on their own, and fully automatically. They replace the process of manual feature engineering, and therefore save a lot of time, effort, and cost. They’re currently being used not only for image classification, video recognition, or adding color to black and white pictures, but also for speech recognition, security enhancement, and detecting intrusion behavior.
CNNs considerably accelerate the speed of development in machine learning, and they allow companies to create stunning applications that make people’s lives easier and more enjoyable. Here are some great examples:
PrettyCity - our Netguru Python-based app in which we use neural network algorithms to detect cranes hanging above buildings, so they can be deleted from pictures, making the images look prettier.
AI Scry - an iOS app that identifies and describes what’s in front of your phone’s camera.
Google Translate - now able to translate speech without transcribing it first.
It’s not an exaggeration to say that developing CNNs was a milestone in the evolution of AI. Of course, there’s still a lot of room for improvement, but being able to simulate activity in the human brain sounds captivating enough to engage even the best specialists on the market in the entire upgrade process.
At Netguru, we are working on this as well, so if you want to learn more about ML's pros and cons, see our article. If you are interested in our Machine Learning projects, we’ll be happy to answer your questions. Or maybe you’re thinking about developing your own ML-enabled app? Anyway, just drop us a line.