A neural network is the fundamental building block of modern deep learning. It consists of layers of artificial neurons that transform input data through learned weights and activation functions to produce outputs. Neural networks can learn complex patterns through training on data.
Key Components
- Input Layer: Receives the initial data
- Hidden Layers: Process information through weighted connections
- Output Layer: Produces the final prediction or classification
- Weights: Learnable parameters that determine connection strength
- Biases: Learnable offsets that help the network fit data better
Applications
Neural networks power image recognition, natural language processing, speech recognition, recommendation systems, and countless other AI applications.
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Related Terms
Activation Function
A non-linear function applied to neuron outputs that introduces non-linearity, enabling networks to learn complex patterns.
Backpropagation
The algorithm for computing gradients of the loss with respect to network weights, enabling training through gradient descent.
Deep Learning
A subset of machine learning that uses neural networks with multiple layers (deep neural networks) to learn hierarchical representations of data.