Table 1.

Comparing deep learning with other machine learning methods

Goal	Examples of Deep Learning Methods	Examples of Other Machine Learning Methods
Regression (Linear)	Single-layer Perceptron with Linear Activations	Linear Regression, Ridge, Lasso
Regression (Nonlinear)	Multilayer Perceptron with Nonlinear Activations	Generalized Linear Model, Polynomial Regression
Regression (Time series, Sequences)	Long Short-term Memory Network, Transformer	Autoregressive models, Hidden Markov Model
Classification	Convolutional Neural Network	Support Vector Machine, Random Forest
Dimension Reduction (Linear)	Autoencoder with Linear Activations	Principal Component Analysis, Exploratory Factor Analysis
Dimension Reduction (Nonlinear)	Autoencoder with Nonlinear Activations, Self-supervised Model	T-distributed Stochastic Neighbor Embedding, Uniform Manifold Approximation and Projection
Clustering	N/A (deep learning can facilitate clustering but does not itself return categorical outputs)	K-Mean, Hierarchical Clustering, Gaussian Mixture Model
Cognitive Models	Spiking Network	Drift Diffusion Model
Agentic Models	Deep reinforcement learning	Reinforcement learning

Examples represent common use cases; they are neither exclusive nor exhaustive