Deep learning is a subset of machine learning. Deep learning is inspired by the human brain. Deep learning is based on artificial neural networks. The “deep” in deep learning refers to the number of layers through which the data has to go through before the output layer. Neural networks use a hierarchy of layered filters.
One of the differences between machine learning and deep learning model is the feature extraction. In machine learning, feature extraction is done by humans whereas a deep learning model figures it out by itself without human intervention.
The performance of Deep learning algorithms continues to increase as we train them with more and more data and construct larger neural networks. Whereas in other machine learning techniques performance reach a plateau after a particular amount of data.
A typical deep learning model has at least three layers. Just like how humans learn from experience, the deep learning models also learn from each iteration and tweak the parameters accordingly to improve the outcome. Each layer learns from the previous layer and then passes its output to the next layer.
Deep learning architectures have been applied to social network filtering, customer support, medical care, self-driving cars, image recognition, financial fraud detection, speech recognition, computer vision, medical image processing, natural language processing, and many other fields.
In short, our lives are influenced by deep learning on a daily basis because deep learning models are everywhere!