Timestep

As explained in Natural Language Processing in Action, Second Edition

A time step is one ordered position in a sequential computation, corresponding here to a token position in text.

A timestep is one ordered position in sequential computation. For text, the recurrent network processes a different token at each position while reusing the same weights. section 8.3

During training, the error travels backward through these positions one token at a time. Each timestep contributes gradients that help adjust the shared weights. section 8.3

Token-by-token processing

For a text sequence, timestep 1 processes the first token, timestep 2 processes the next, and so on. The same neural network is reused at every position. section 8.3

Figure 8.10 Data fed into a recurrent network

Inspect the recurrent-network input layout to connect token positions with ordered timesteps. section 8.3

How Natural Language Processing in Action, Second Edition covers it

How deep it goes

Explains shared weights and serial backpropagation across tokens, without detailing tensor shapes or implementation code.

Examples

Uses text tokens, an unrolled recurrent network, and token-by-token gradient propagation as concrete illustrations.

What it assumes

Assumes familiarity with tokens and neural-network weights while grounding timesteps in recurrent processing.

What you can do after

Understand how recurrent networks reuse weights and propagate error across ordered token positions.

How it connects

Connects timesteps to tokens, recurrent neural networks, shared weights, and backpropagation through time.

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Also explained in

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  Grokking AI Algorithms, Second EditionA timestep is one discrete point in the sequence of stages used to progressively add or remove noise in a diffusion process.
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  Build a Text-to-Image Generator (from Scratch)A time step is a discrete stage in the diffusion process at which a particular noise level is applied.
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  Inside Deep Learning: Math, Algorithms, ModelsA discrete position in a sequence or recurrent computation at which the model updates state.
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  Learn Generative AI with PyTorchA discrete index into the diffusion/denoising schedule that determines how much noise is added or removed at a given stage.

Compare how 5 books explain timestep →

Related concepts

• simulationused by · A simulation is a modeled or controlled enactment of a system, process, or scenario used to examine or reproduce behavior under specified conditions.
• U-Netused by · U-Net is an encoder-decoder neural-network architecture with downsampling and upsampling paths connected by skip connections for combining features at multiple spatial scales.
• diffusion modelpart of · A diffusion model is a generative model that learns to produce data by reversing a process that progressively adds noise to data.
• timestep embeddingproduces · A timestep embedding is a numerical vector representation of a diffusion process timestep used to condition a denoising model.
• denoisingpart of · Denoising is the process of removing unwanted noise or corruption from data while preserving meaningful structure.

This explainer was generated by Manning's knowledge graph from the text of Natural Language Processing in Action, Second Edition. The section links point to the parts of the book it draws on.