What is a neural network? Weights, hidden layers, and nonlinear computation
Understand neural networks through handwritten-digit classification, including inputs, layers, outputs, learned weights, and checks for reliable predictions.
Understand the concept
A neural network connects computational units in layers, using weights and activation functions to transform inputs into outputs. Training adjusts parameters to fit a task objective. The neuron analogy does not make it a biological brain or the only form of AI. More layers or parameters alone do not establish better task results.
Follow computation from pixels to class predictions
For handwritten digits, inputs can be pixel values and outputs class scores. Intermediate layers transform values, and nonlinear activations represent patterns beyond simple linear relationships. This illustrates architecture without training a recognizer here.
Connections can have trainable weights, and units may use biases. Parameters determine how inputs affect computation; architecture defines the connections. A network diagram does not reveal learned parameters, and visual complexity does not establish accuracy.
Training updates parameters using loss and gradients
Training compares predictions with labels, calculates loss, backpropagates gradients, and uses an optimizer to update parameters. Loss functions and task settings define the objective. Producing a number does not establish completed training or a reliable result.
If training images share a background, a network may learn that cue instead of digit shape. Good training-set performance can fail on new images. Hold out data and include different capture conditions to inspect concrete errors.
Distinguish deep learning from networks and rules
Deep learning generally uses multilayer networks, while networks can also be shallow. Rule-based programs use explicitly written conditions. Applications can combine them: a model recognizes an image, then rules check allowed output ranges before further processing.
Specify input types and error costs, including whether uncertain cases may be rejected. A classification score is not automatically real-world accuracy; probability calibration needs separate evaluation. This page explains mechanisms from teaching sources without benchmark results or product recommendations.
Checked sources
These sources support the concepts and methods described here. Examples and test scope are labeled separately; check the version of the tools you use.