Neural Network
A neural network is a computing model inspired by biological neurons: layers of connected nodes that process inputs with learned weights and nonlinear functions. They are the building blocks of modern deep learning.
In Simple Terms
Think of it as a team of filters: each layer passes information to the next, refining the signal until the network produces an answer.
Detailed Explanation
Each layer transforms its input; early layers often capture low-level features (edges, words), and deeper layers capture more abstract patterns. Training adjusts the weights (and sometimes architecture) via backpropagation and gradient descent. Neural networks scale with data and compute and power most of today's AI applications. Architectures vary: feedforward networks, convolutional networks (vision), and transformers (language and beyond). Understanding the basics helps with model choice, debugging, and communicating with technical teams.
Related Terms
Artificial Intelligence
The simulation of human intelligence processes by machines, especially computer systems.
Read moreTransfer Learning
Transfer learning is the practice of taking a model already trained on one task or dataset and reusing or adapting it for another. It speeds development and often improves performance when data for the new task is limited.
Read moreMachine Learning
A subset of AI that enables systems to learn and improve from experience without being explicitly programmed.
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