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What is the difference between Deep Learning and Machine Learning?If deep learning is a subset of machine learning, how do they differ? Deep learning distinguishesitself from classical machine learning by the type of data that it works with and the methods in whichit learns.Machine learning algorithms leverage structured, labelled data to make predictions—meaning thatspecific features are defined from the input data for the model and organized into tables. Thisdoesn’t necessarily mean that it doesn’t use unstructured data; it just means that if it does, itgenerally goes through some pre-processing to organize it into a structured format.Deep learning eliminates some of data pre-processing that is typically involved with machinelearning. These algorithms can ingest and process unstructured data, like text and images, and itautomates feature extraction, removing some of the dependency on human experts. For example,let’s say that we had a set of photos of different pets, and we wanted to categorize by “cat”, “dog”,“hamster”, et cetera. Deep learning algorithms can determine which features (e.g., ears) are mostimportant to distinguish each animal from another. In machine learning, this hierarchy of features isestablished manually by a human expert.Then, through the processes of gradient descent and backpropagation, the deep learning algorithmadjusts and fits itself for accuracy, allowing it to make predictions about a new photo of an animalwith increased precision.Machine learning and deep learning models are capable of different types of learning as well, whichare usually categorized as supervised learning, unsupervised learning, and reinforcement learning.Supervised learning utilizes labelled datasets to categorize or make predictions; this requires somekind of human intervention to label input data correctly. In contrast, unsupervised learning doesn’trequire labelled datasets, and instead, it detects patterns in the data, clustering them by anydistinguishing characteristics. Reinforcement learning is a process in which a model learns to becomemore accurate for performing an action in an environment based on feedback in order to maximizethe reward.
