What is a neural network?
A neural network (often called an artificial neural network or ANN) is a stack of simple math layers that learn to turn inputs into useful outputs. Each layer has many tiny units (“neurons”). Every neuron multiplies its inputs by weights, adds a bias, and passes the result through an activation function (like ReLU or sigmoid). By chaining many layers together—deep learning—the model can learn complex patterns in images, text, audio, and tabular data.
How it learns: during training, the network makes a prediction, compares it to the correct answer with a loss function, and then uses backpropagation with gradient descent to nudge the weights in the right direction. Do this over and over on labeled training data, and the network gradually gets better. When you use the finished model to make predictions on new data, that’s called inference.
Why this is powerful?
Neural networks are powerful because they learn features automatically. In vision, early layers find edges and textures while deeper layers respond to objects; in language, layers move from characters and words to phrases and meaning.
Different architectures are tuned to different data: feedforward networks (MLPs) handle general prediction tasks, convolutional networks (CNNs) excel at images and video, recurrent networks (RNNs, LSTMs, GRUs) model sequences and time series, and transformers now lead in language and increasingly in vision, audio, and multimodal work.
To avoid overfitting, practitioners use techniques like regularization, dropout, and early stopping, and they track progress on a separate validation set before final testing.
Example:
Scenario: Classify product photos as t-shirt, shirt, or jacket.
- Collect a labeled dataset of images for each category.
- Feed the pixels into a small CNN: convolution → nonlinearity → pooling → a few blocks → final linear layer → softmax probabilities.
- Train with cross-entropy loss and gradient descent; monitor accuracy on a validation split and stop when it plateaus.
- Export the model and run inference on new images to tag incoming products automatically.
Outcome: The model learns its own visual features (collars, sleeves, zippers) and assigns the most likely category, boosting catalog quality and search.