Explanation:

Statement 1: Deep learning is a machine learning technique that teaches computers to do what comes naturally to humans: learn by example.
It is based on learning data representations, as opposed to task-specific algorithms.

In deep learning, a computer model learns to perform classification tasks directly from images, text, or sound.
Deep learning models can achieve state-of-the-art accuracy, sometimes exceeding human-level performance. Models are trained by using a large set of labeled data and neural network architectures that contain many layers.
Statement 3: Deep learning architectures such as deep neural networks, deep belief networks and recurrent neural networks have been applied to fields including computer vision, speech recognition, natural language processing, audio recognition, social network filtering, machine translation, bioinformatics and drug design, where they have produced results comparable to and in some cases superior to human experts.
For e.g. In medical informatics, deep learning was used to predict sleep quality based on data from wearables.
Learning: Deep learning is a key technology behind driverless cars, enabling them to recognize a stop sign, or to distinguish a pedestrian from a lamppost.
It is the key to voice control in consumer devices like phones, tablets, TVs, and hands-free speakers. Deep learning is getting lots of attention lately and for good reason. It’s achieving results that were not possible before.
The new exoplanets have been discovered using a deep learning neural network — an artificial intelligence tool that mimics the workings of a human brain.