If we say that deep learning is a technique by which a computer model learns to handle certain situations like a human, it’ll be an oversimplification.
There is actually no easy way to put it but using the phrase “learning by example”. In a sense, the phrase does make sense if you consider the extensive range of data and neural network architectures as examples.
But if you want to get the technical terms right then Deep Learning will be a machine learning method, which is based on learning data representations, contrary to the traditional task-specific algorithms.
Deep learning is the key technology contributing mostly to driverless cars, which enabled the cars to recognize street signs and differentiate between a living being and a lamppost. Other voice-activated devices like tablets, phones, hands-free speakers, TVs, etc. are also benefiting from this technology.
Most of the deep learning techniques use neural network architectures, and that is why deep learning structures and models are referred to very often as Deep Neural Networks.
In the deep learning process, a computer setup identifies a specific sound, text or image and then performs tasks accordingly. To someone with no knowledge about deep learning, this will feel like a human-like nature, learning and acting according to the situation. But what actually happened here is these computer models were pre-trained with a wide range of data and neural network architecture, which contains multiple layers.
The Specific term “Deep” in Deep Learning usually refers to the number of layers in the neural network. The number of layers in deep learning can be as much as 150.
Nowadays, Deep Learning has achieved so much accuracy that some may call it Human-like. This is helping consumer electronics to meet their user’s expectations and ensuring ease of use for them. They gained our trust to an extent where we let it handle complicated and safety-critical things like driving an actual car. The recent advancement has proved its potential to even outperform humans in cases like identifying objects in images.
But the advancement didn’t happen in a click. Behind the accuracy of deep learning lies a humongous amount of labeled data, like millions of photos and thousands of hours of videos for driverless car development. Another requirement for deep learning is substantial computing power. As high-performance GPUs have a parallel architecture, they are definitely efficient for deep learning. And when these superfast GPUs combine with cloud computing, the week of development time cuts down to only a matter of hours, allowing the development team to focus on more advancements for the future.