Feedforward network

As explained in Grokking Machine Learning

A neural network composed of multiple layers of perceptron-like processing units.

A neural network, also called a multilayer perceptron, combines perceptron-like units across layers. Viewed as a composition of linear classifiers, it can produce nonlinear decision boundaries. section 10.0

From simple to complicated images

A perceptron can classify curated handwritten-digit images such as MNIST, but performs poorly on more complicated images. Combining many perceptrons offers another approach to image recognition. section 5.5

Figure 10.1 A neural network. It may look complicated, but in the next few pages, we will demystify this image.

Use the neural-network figure as a visual starting point for recognizing its nodes and connections. section 10.0

How Grokking Machine Learning covers it

How deep it goes

Defines networks as perceptron collections and previews layer architecture, activation functions, backpropagation, training problems, and remedies.

Examples

Uses dog-image recognition and MNIST digit recognition as classification examples, and names sentiment analysis and image classification applications.

What it assumes

Builds on perceptrons and logistic classifiers while briefly refreshing discrete and continuous perceptron terminology.

What you can do after

Understand how composing linear classifiers can yield nonlinear decision boundaries and identify classification and regression uses.

How it connects

Connects perceptrons and logistic classifiers to linear and nonlinear classifiers, backpropagation, regularization, and dropout.

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Also explained in

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  Sutskever's ListA feed-forward network is a position-wise neural network that transforms each sequence representation independently, typically through linear layers and a nonlinear activation.
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  Build a Text-to-Image Generator (from Scratch)A feed-forward network that applies the same transformation independently to each position in a sequence.
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  Classic Computer Science Problems in Swift: Essential techniques for practicing programmersA feed-forward network is a neural network in which signals move through successive layers in a single direction without cycles.
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  Deep Learning with R, Third EditionA feedforward block is a neural-network subnetwork that expands a representation, applies a nonlinearity, and projects it back to the model dimension.
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  Grokking AI Algorithms, Second EditionA feed-forward layer transforms its inputs through learned parameters without recurrent state or feedback connections.
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  Machine Learning Algorithms in DepthA feed-forward network is a neural network whose computation flows directly from inputs to outputs without recurrent state.
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  Transformers in ActionA feed-forward network is a neural-network component that transforms inputs through successive fully connected layers without recurrent state.
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  Deep Learning with Python, Third EditionThe Transformer submodule that applies a position-wise dense transformation and nonlinearity to each token representation.
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  Build a Large Language Model (From Scratch)The transformer subcomponent that applies position-wise nonlinear transformations after attention.
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  LLMs in ProductionA feed-forward layer is a neural-network layer that transforms its input through learned operations without recurrent state.

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Related concepts

• transformer architecturepart of · A transformer architecture is a neural-network architecture that processes sequences or other structured inputs using attention mechanisms and related transformations.
• language modelkind of · A language model is a computational model that represents patterns in linguistic tokens to estimate sequence probabilities and predict or generate language from context.
• recurrent neural networkcontrasts with · A recurrent neural network is a neural network that processes sequences by updating an internal state across successive inputs, allowing earlier inputs to influence later computations.
• transformer blockpart of · A transformer block is a neural-network module that transforms sequence representations using attention and feed-forward computation.
• linear layerhas part · A linear layer is a neural-network layer that maps input features to outputs by a learned affine transformation, typically connecting each output to every input feature.
• mixture of expertsalternative to · A mixture-of-experts model is a neural-network architecture with specialized expert subnetworks and a routing mechanism that selects or combines experts for each input.

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