Deep learning is a type of machine learning and artificial intelligence (AI) that imitates the way humans gain certain types of knowledge. Deep learning is an important element of data science, which includes statistics and predictive modeling. It is extremely beneficial to data scientists who are tasked with collecting, analyzing and interpreting large amounts of data; deep learning makes this process faster and easier.
To understand deep learning, imagine a toddler whose first word is dog. The toddler learns what a dog is – and is not – by pointing to objects and saying the word dog. The parent says, “Yes, that is a dog,” or, “No, that is not a dog.” As the toddler continues to point to objects, he becomes more aware of the features that all dogs possess. What the toddler does, without knowing it, is clarify a complex abstraction – the concept of dog – by building a hierarchy in which each level of abstraction is created with knowledge that was gained from the preceding layer of the hierarchy.
At its simplest, deep learning can be thought of as a way to automate predictive analytics. While traditional machine learning algorithms are linear, deep learning algorithms are stacked in a hierarchy of increasing complexity and abstraction.
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Thanks @Mansi_Mantri for this nice summary
For the record, it’s an unattributed cut and paste from the web. 