Neural Networks Learning Path

Neural network components and signal flow

Neural networks are built from interconnected artificial neurons organized into layers. Each neuron receives real-valued signals from neurons in the previous layer through weighted connections, sums (or otherwise aggregates) those inputs, and applies a nonlinear activation function to produce its output. Signals propagate from an input layer through one or more hidden layers to an output layer, and a network is considered “deep” when it has at least two hidden layers, enabling it to learn hierarchical representations. The signal flow is determined by the network’s architecture. In feedforward networks (e.g., multilayer perceptrons), information moves in one direction from inputs to outputs. Convolutional neural networks (CNNs) use convolutional layers and downsampling to detect local patterns while reusing weights across spatial locations. Recurrent neural networks (RNNs) introduce looped connections so a hidden state can carry information across time steps, making them suitable for sequential data. Transformers use attention mechanisms to relate every token to every other token, supporting modeling of long-range dependencies. During training, the weights are adjusted to reduce prediction error. A common approach is backpropagation, which efficiently computes how changes in weights affect the loss by propagating error gradients backward through the layers. This component-wise signal flow—forward computation of activations and backward computation of gradients—enables optimization of the network parameters using data and a chosen loss function.

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Artificial neurons, weights, and activation functions

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