How deeply does Data Without Labels cover deep belief network?

Explains RBM layers, contrastive divergence, Gibbs sampling, layer-wise pretraining, and fine-tuning, while noting optimization challenges.

Deep belief network

As explained in Data Without Labels

A deep belief network is a generative neural-network model composed of multiple layers of stochastic latent variables, typically structured as restricted Boltzmann machines.

A deep belief network (DBN) is a generative neural-network model composed of multiple layers of stochastic binary latent variables. Each layer is a restricted Boltzmann machine (RBM): its visible units represent observed data, while its hidden units learn features. section 8.9 section 8.9.1

Train a DBN through two connected phases:

Pretrain the RBMs layer by layer without labeled data. The first layer captures lower-level features, while later layers learn increasingly abstract representations. section 8.9.1
Use contrastive divergence to approximate each layer’s log-likelihood gradient through Gibbs sampling, adjusting weights to model the input distribution. section 8.9.1
Fine-tune the complete network with labeled data and backpropagation, minimizing prediction error for a supervised task such as classification. section 8.9.1

This staged approach can learn complex representations for image or speech recognition and organize unlabeled data. Computational expense, overfitting, vanishing gradients, and intricate fine-tuning can limit performance. section 8.13

Recognition pipeline

Feed unlabeled inputs into successive RBMs to learn increasingly abstract features, then fine-tune the network with labeled examples for classification, image recognition, or speech recognition. section 8.9.1 section 8.13

How Data Without Labels covers it

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Related concepts

restricted Boltzmann machinehas part · A restricted Boltzmann machine is a generative probabilistic neural-network model whose visible and hidden units are connected across layers but not within a layer.
generative modelingkind of · Generative modeling is a machine-learning approach that learns patterns or a data distribution to generate new samples resembling the data.
generative adversarial networkkind of · A generative adversarial network is a generative model with a generator and discriminator trained in competition, enabling the generator to produce samples resembling a target dataset or distribution.
latent variablehas part · A latent variable is an unobserved quantity represented or inferred by a model to capture underlying structure in observed data.
unsupervised learningused by · Unsupervised learning is a machine-learning approach that discovers patterns, structure, or representations in data without labeled target outputs.
pretrainingused by · Pretraining is the initial training of a model on broad data to learn general patterns or capabilities before adaptation to specific tasks or domains.

This explainer was generated by Manning's knowledge graph from the text of Data Without Labels. The section links point to the parts of the book it draws on.