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Deep Learning: Comparison with Machine Learning

Deep learning is sometimes referred to as the intersection between machine learning and artificial intelligence. It is about designing algorithms that can make robots intelligent, such a face recognition techniques used in drones to detect and target terrorists, or pattern recognition / computer vision algorithms to automatically pilot a plane, a train, a boat or a car.

Many deep learning algorithms (clustering, pattern recognition, automated bidding, recommendation engine, and so on) -- even though they appear in new contexts such as IoT or machine to machine communication -- still rely on relatively old-fashioned techniques such as logistic regression, SVM, decision trees, K-NN, naive Bayes, Bayesian modeling, ensembles, random forests, signal processing, filtering, graph theory, gaming theory, and many others.

Example of deep learning algorithms for clustering

As a result, many deep learning practitioners call themselves data scientist, computer scientist, statistician, or sometimes engineer. In my opinion, deep learning also tries to automate some data science processes.

Some opinions expressed in this article may be those of a guest author and not necessarily Analytikus. Staff authors are listed

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