Backpropagation

As explained in Grokking AI Applications

Backpropagation is an optimization procedure that propagates prediction error through a neural network to adjust its parameters.

Backpropagation is an iterative learning process. A neural network begins with random parameters, makes a prediction, compares it with the expected result, and adjusts its parameters slightly to reduce future errors. section 1.6

A training pass follows three practical stages:

• Make a prediction using the network’s current parameters. section 1.6
• Identify whether the prediction is wrong. section 1.6
• Adjust the parameters slightly and repeat across many training examples. section 1.6

Figure 1.12 The two stages of GPT-3. First, it gets trained, and then the sentence completion is inferred.

Separates GPT-3’s training stage, where parameter adjustment occurs, from inference, where the trained model completes sentences. section 1.6

How Grokking AI Applications covers it

How deep it goes

Defines the repeated prediction-and-adjustment loop but stops short of mathematical gradients or implementation details.

Examples

Uses student guesses, text completion, GPT-3’s 45 TB dataset, and GPUs as intuitive training examples.

What you can do after

Understand how repeated parameter adjustments reduce errors during neural-network training.

How it connects

Connects neural networks with parameters, training examples, prediction, inference, pretrained models, and GPUs.

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  Deep Learning with PyTorch, Second EditionA PyTorch autograd call that performs reverse-mode differentiation.
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  Machine Learning Algorithms in DepthBackpropagation computes gradients of an objective with respect to parameters by propagating derivatives backward through a computational composition.
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Related concepts

• gradientproduces · A gradient is the vector of partial derivatives of a scalar function with respect to its variables, indicating its local rate and direction of change.
• chain ruleuses · The chain rule is a calculus rule for differentiating a composite function by multiplying derivatives along its composition.
• optimizationimplemented by · Optimization is the systematic selection or adjustment of values, actions, or system designs to improve a specified objective, often subject to constraints.
• objective functionuses · An objective function assigns values to candidate solutions, predictions, or parameter settings according to a criterion to be minimized or maximized.
• language modelused by · A language model is a computational model that represents patterns in linguistic tokens to estimate sequence probabilities and predict or generate language from context.
• forward passcontrasts with · A forward pass is a calculation that propagates inputs through an ordered computational or dependency structure to produce outputs or derived values.

This explainer was generated by Manning's knowledge graph from the text of Grokking AI Applications. The section links point to the parts of the book it draws on.