What is Deep Learning?
Deep learning is a machine learning technique which teaches computers to do what comes naturally occurs to humans: learn by example. It is the important technology behind driverless cars, enabling them to recognize a stop sign, or to distinguish a pedestrian from a lamppost. It is important for voice control in consumer devices like phones, tablets, TVs, and hands-free speakers.
It’s getting results that were not possible before. In deep learning, a computer model gets to perform classification tasks directly from images, text, or sound. Deep learning models can achieve state-of-the-art excellency, sometimes exceeding human-level performance. Training of Models with a large set of labeled data and neural network architectures contains numerous layers.
How does deep learning attain such impressive results?
It was first theorized in the 1980s, there are two main reasons it has only recently become widely accepted:
DL requires huge amounts of labeled data. For example, driverless car development requires millions of images and thousands of hours of video.
DL requires substantial computing power. High-performance GPUs have a parallel architecture that is important for deep learning. When combined with cloud computing, this enables development teams to reduce training time for a deep learning network from weeks to hours or less.
How DL Works?
Most methods uses neural network architectures, which is the reason why deep learning models are often referred to as deep neural networks.
The term “deep” usually refers to the number of ambushed layers in the neural network. Traditional neural networks only contains 2-3 hidden layers, while deep networks can have as many as 150.
Training of Deep learning models by using large sets of labeled data and neural network architectures learns features directly from the data without the requirement for manual feature extraction.
Ways it is in practice?
- Customer experience
Using of DL by many businesses helps to uplift the customer experience. Just a couple of examples include online self-service solutions and creation of reliable workflows. Using of models for chatbots, and continue to mature, we can expect this to be an area DL which will be using for many businesses.
Automatic machine translation is new, deep learning is helping improve automated translation of text by using stacked networks of neural networks and allowing translations from images.
- Adding color to black-and-white images and videos
What used to be a very cumbersome process where humans had to add color to black-and-white images and videos by hand can now be automatically made with deep-learning models.
- Language recognition
Models are beginning to differentiate dialects of a language. A machine could decide that someone is speaking English and then envisages an AI that is learning to tell the differences among dialects. Determining the dialect , another AI will step in that specializes in that particular dialect. All of this happens without human interference.
- Autonomous vehicles
Some models specialize in streets signs while training others to identify pedestrians. Mentioning about a car navigation down the road, up to millions of individual AI models allows the car to act.