What is a Neural Network?
A layered system that processes information in stages, with each layer detecting slightly more complex patterns than the last.
Neural Network explained in plain English
A neural network is a layered system that processes information in stages, with each layer detecting slightly more complex patterns than the last. Simple features combine into richer understanding as data passes through.
These networks are the backbone of deep learning and power most modern AI applications.
Analogy
Imagine a committee where the first person notices basic shapes, the next person combines those into objects, and a final person draws a conclusion. Each member only sees the summary from the one before — together they reach a decision no single person could make alone.
Example
When your phone unlocks using your face, or when a translation app converts speech in real time, a neural network is typically doing the heavy lifting.
How is Neural Network used?
Neural networks power face recognition on phones, language translation, medical scan analysis, and the core of large language models like GPT.
Common misconceptions about Neural Network
Despite the name, neural networks are not models of the human brain — they are mathematical structures inspired by a simplified idea of how neurons connect.
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- Backpropagation
The process that tells a neural network which internal settings caused an error and how to adjust them, working backwards through layers.
- Gradient Descent
The method by which a model gradually improves by making small adjustments after each mistake, moving toward better performance.
- activation function
A function that enables neural networks to learn nonlinear (complex) relationships between features and the label.
- Artificial Intelligence
Any system that performs tasks requiring human judgment — understanding language, recognising images, making decisions, or solving problems — by finding patterns in data rather than thinking or feeling.
- autoencoder
A system that learns to extract the most important information from the input.
- batch
The set of examples used in one training iteration.
- batch normalization
Normalizing the input or output of the activation functions in a hidden layer.
- batch size
The number of examples in a batch.
- Bayesian neural network
A probabilistic neural network that accounts for uncertainty in weights and outputs.
- Classification
The task of sorting inputs into predefined categories — choosing a label rather than producing a number.