From the course: Artificial Intelligence Foundations: Neural Networks
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Layers: Input, hidden, and output
From the course: Artificial Intelligence Foundations: Neural Networks
Layers: Input, hidden, and output
- [Instructor] In this video, you learn about neural network layers, which allow neural networks to solve complex non-linear problems. This image shows an input layer, hidden layer, and output layer. The middle layer is always referred to as hidden as it hides between the input and output layers. The layers are composed of nodes stacked on top of each other and are connected input to output until the final output node. Signals go from an input layer to additional layers. Each layer is fully connected to the other. There are no loops. This is what it means when you see the term fully connected. You feed your data or features into the first layer because it is used to provide the input data or features to the network. Thus, it is referred to as the input layer. The input layer is the very beginning of the workflow for the artificial neural network. The input layer is considered passive because it does not take in…
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