Chapter 28 sections from Deep Learning with PyTorch.
6 items
A Boltzmann machine is a probabilistic neural network that defines a probability distribution over binary variables.
A restricted Boltzmann machine, or RBM, is a simplified Boltzmann machine with a bipartite structure.
A deep belief network, or DBN, is a probabilistic generative model formed by stacking multiple layers of latent variables.
Energy-based models, or EBMs, define probability distributions using energy functions rather than normalized output probabilities directly.
Flow-based models are generative models that learn an invertible transformation between a simple probability distribution and a complex data distribution. Unlike many other generative models, flow-based systems provide:
Probabilistic circuits are tractable probabilistic models built from simple computational graphs.