Understanding Artificial Neural Networks

The Basic Architecture

ANNs are typically organized into layers – an input layer, one or more hidden layers, and an output layer. Each neuron in a layer is connected to neurons in the next layer. This layered structure allows for complex pattern recognition.

The input layer receives raw data (e.g., pixel values of an image). Hidden layers perform computations on this data, transforming it into a representation suitable for the task. The output layer produces the final result.

Activation Functions

Neurons in ANNs don't simply output the weighted sum of their inputs. Instead, they use an activation function to introduce non-linearity. This is crucial for learning complex patterns.

Common activation functions include sigmoid (σ(x) = 1 / (1 + e^(-x))), ReLU (Rectified Linear Unit - f(x) = max(0, x)), and tanh (tanh(x)). Each has different properties affecting training speed and model performance.

σ(x) = 1 / (1 + e^(-x))

Learning Through Backpropagation

The most common method for training ANNs is backpropagation. It involves calculating the error between the network’s output and the desired output.

This error is then propagated backward through the network, adjusting the weights of the connections to minimize the error. This iterative process continues until the network learns to produce accurate outputs.

Δw = η * ∂Loss/∂w

Key Concepts & Terminology

**Weights:** Numerical values associated with each connection, determining the strength of the signal passed between neurons. These are adjusted during training.

**Bias:** A constant value added to the weighted sum of inputs, allowing the neuron to activate even when all inputs are zero.

Frequently asked questions

Try it live

Everything above runs in your browser — open SPH Fluid and change the parameters while it is running. Nothing is installed, nothing is uploaded, the whole model lives in one tab.