Input layer

As explained in Inside AI

An input layer is the neural-network layer that receives the input data.

An input layer receives the data fed into a neural network. It begins the flow through an architecture that typically includes input, hidden, and output layers. section 3.3

A three-value input

A backpropagation network can receive the values 0.5, 0.6, and 0.2 through its input layer. Nodes pass these values to the next layer through weighted connections; training later adjusts those weights when predictions differ from correct labels. section 3.3

Figure 3.3 A backpropagation model with three layers

Use the three-layer backpropagation diagram to trace data from the input layer through hidden layers to the output layer. section 3.3

How Inside AI covers it

How deep it goes

Defines the layer and illustrates its values and connections without detailing its internal computation.

Examples

Uses input values 0.5, 0.6, and 0.2 in a three-layer backpropagation structure.

What you can do after

Evaluate how data enters a neural network and passes toward prediction through connected layers.

How it connects

Connects input data to hidden layers, output layers, artificial neurons, weights, thresholds, and backpropagation.

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Related concepts

• deep learningpart of · Deep learning is a machine-learning approach that uses neural networks with multiple computational layers to learn representations from data.
• neural network layerkind of · A neural network layer is a computational stage or component that transforms input representations into outputs within a neural network.
• convolutional neural networkpart of · A convolutional neural network is a neural-network architecture that applies learned convolutional operations to extract patterns from structured data.
• Mapconfigured by · A map is an associative data structure that stores key-value pairs and supports retrieving values by key.
• hidden layercontrasts with · A hidden layer is an internal neural-network layer between the input and output layers that transforms intermediate representations.

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