Input layer
An input layer is the neural-network layer that receives a model’s input data or features and provides their entry point to subsequent processing.
Classic Computer Science Problems in Swift: Essential techniques for practicing programmers
An input layer is the first neural-network layer that receives input signals and passes them into the network.
- How it is used
- Receives feature values and passes them into a feed-forward classifier.
- How deep it goes
- Defines the layer and shows its Swift pass-through implementation, while deferring calculus-based derivations.
- Examples
- Uses pixel intensities from a 10×10 image and four iris measurements feeding a [4, 6, 3] network.
- What you can do after
- Build it from scratch by sizing a Swift input layer to its features and passing its values to the next layer.
Grokking Machine Learning
The neural-network layer that receives the input features or input representation.
- How it is used
- Receives the features or representation supplied to a neural network.
- How deep it goes
- Defines its position and layer size, then demonstrates declaring input shapes in Keras.
- Examples
- Uses two-feature data and flattened handwritten-digit images as input representations.
- What you can do after
- Apply it via Keras by setting `input_shape` to match the dataset's feature representation.
Inside AI
An input layer is the neural-network layer that receives the input data.
- How it is used
- Receives input data at the start of a backpropagation network.
- 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.
Data Without Labels
An input layer is the first neural-network layer that receives input data and distributes it to subsequent layers.
- How it is used
- Receives raw or processed data and starts its forward flow through hidden layers toward a prediction.
- How deep it goes
- Explains the layer’s position and data flow, but defers specialized connection patterns beyond the basic network structure.
- Examples
- Uses image classification, face recognition, raw images, processed images, and image tensors with color channels.
- What you can do after
- Evaluate/understand how data enters a neural network and flows toward image or identity predictions.
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.
These guides are generated from the text of Manning books by Manning's knowledge graph and link to the sections they draw on. The full books are available on liveBook and at manning.com.