How deeply does Data Without Labels cover pretraining?

Explains layer-wise RBM training, contrastive divergence, Gibbs sampling, and subsequent backpropagation-based fine-tuning.

Pretraining

As explained in Data Without Labels

Pretraining is an initial model-training stage that learns general patterns or representations before adaptation to a specific task.

Pretraining is an initial, typically unsupervised stage that learns useful representations before supervised fine-tuning for a downstream task. In deep belief networks, each layer first learns from the data distribution, preparing weights that can support classification, regression, image recognition, or speech recognition. section 8.13 section 8.9.1

A layer-wise pretraining workflow proceeds as follows:

Train the first restricted Boltzmann machine with unlabeled data to capture low-level features. section 8.9.1
Use the learned representation as input to the next restricted Boltzmann machine, which learns increasingly abstract features. section 8.9.1
Train each layer separately with contrastive divergence, using Gibbs sampling to approximate the log-likelihood gradient. section 8.9.1
Fine-tune the complete network with backpropagation and labeled data to reduce prediction error. section 8.9.1

Image or speech recognition

A pretrained deep belief network can learn basic patterns in early layers and more complex features in later layers before supervised fine-tuning for recognition. section 8.13 section 8.9.1

How Data Without Labels covers it

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Also explained in

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Deep Learning with R, Third EditionUnsupervised pretraining is training a model on input data without manually supplied labels before adapting it to downstream tasks.
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Deep Learning with Python, Third EditionThe initial large-scale training phase used to learn broad language representations before task-specific fine-tuning.
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Build a Large Language Model (From Scratch)The initial training phase in which a language model learns general patterns from a large text corpus before task-specific adaptation.
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Time Series Forecasting Using Foundation ModelsPretraining is initial training of a model on a large dataset to learn general patterns and relationships before applying it to specific tasks.
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Inside Deep Learning: Math, Algorithms, ModelsA strategy of training a model on a source dataset before adapting it to a downstream task.
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Build a Reasoning Model (From Scratch)The initial training stage in which a language model learns general statistical structure from large amounts of data before task-specific adaptation.
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The Art of AI Product DevelopmentPretraining is the initial training of a model on broad data before adapting it to specific tasks or domains.
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How Large Language Models WorkPretraining is the initial training of a model on broad data before adapting it to particular tasks or applications.
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Introduction to Generative AI, Second EditionThe initial training phase in which a model learns general representations or predictive behavior from a large corpus before task-specific adaptation.
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LLMs in ProductionPretraining is the initial training of a model on a large, broad dataset before adapting it to a specific task or domain.
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Sutskever's ListPretraining is the initial training of a model on a broad dataset before task-specific adaptation or evaluation.
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Build a Website with ChatGPTPretraining is the initial training of a model on large amounts of data to learn general patterns before task-specific use.
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Reinforcement Learning from Human FeedbackPretraining is the initial large-scale optimization of a model on broad data, commonly through next-token prediction.

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Related concepts

foundational modelproduces · A base or foundation model is a pretrained model that provides learned parameters or capabilities as a starting point for reuse or adaptation to downstream tasks.
pretrained modelproduces · A pretrained model is a machine-learning model whose parameters were learned through prior training before it is used or adapted for a subsequent task.
next-token predictionuses · Next-token prediction is the task or training objective of estimating the token that follows a sequence, given its preceding tokens and any conditioning information.
datasetuses · A dataset is an organized collection of related data items, records, or observations treated as a unit for storage, processing, analysis, or computation.
zero-shot learningused by · Zero-shot learning is a setting in which a model performs a task or predicts categories without task-specific examples during training, adaptation, or inference, relying on learned knowledge and available task information.
similarity searchused by · Similarity search is a retrieval technique that finds or ranks items by comparing their representations with a query according to a similarity or distance measure.

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.