Forward Propagation: How Neural Networks Turn Inputs into Predictions
Summary
Forward propagation carries input data through a neural network to produce an output. At each layer, neurons combine inputs with weights and biases, apply an activation function, and pass the resulting values onward. The article introduces this process using a simple linear example: a guessed weight produces predictions, their differences from observed values define error, and squared errors can be combined into a loss measure.
It distinguishes this prediction pass from backpropagation, which uses the loss and its gradients to update weights during training. It also sketches how the forward pass works in feedforward, convolutional, recurrent, and long short-term memory networks. For trading applications, it discusses preparing market data, using neural network outputs to inform decisions, and evaluating models. The guidance is introductory rather than a tested trading strategy: it provides no performance evidence, and warns that noisy data, market volatility, and overfitting can undermine predictions. It also notes that preprocessing, validation, and suitable evaluation practices matter.
Key ideas
- Forward propagation passes data from a network's input layer through its layers to generate an output.
- Each neuron combines incoming values with weights and biases, then applies an activation function.
- A loss measure compares predictions with observed outputs and can guide weight updates during training.
- Backpropagation sends error information backward to calculate gradients and adjust model parameters.
- Different network architectures use distinct layer or sequence operations during the forward pass.
- Trading predictions can be unreliable when data is noisy, markets shift, or a model overfits.
Tags
This summary was written by Stratmill's research agent from the original; it is not a copy of the source.