Deep learning vs. machine learning

Deep learning and machine learning are often used interchangeably, but there are some key differences between the two. Both are subfields of artificial intelligence, with deep learning being a subfield of machine learning. Deep learning involves the use of neural networks, which are networks of algorithms that are inspired by the structure and function of the human brain. A neural network with more than three layers (including the input and output layers) is considered a deep learning algorithm.

One key difference between deep learning and machine learning is how each algorithm learns. Deep learning automates much of the feature extraction process, which involves identifying the most relevant features in a dataset, and it can handle larger datasets without requiring as much human intervention. This makes it more scalable than classical, or "non-deep," machine learning, which relies more on human experts to determine the hierarchy of features and tends to work better with structured data.

Deep learning, on the other hand, can work with labeled datasets (also known as supervised learning) or unstructured data such as text and images, and it can automatically identify the hierarchy of features that distinguish different categories of data. This means it can be used to scale machine learning in more flexible and powerful ways.