A neural network forward pass is the process of feeding input data through the network to produce a prediction or output. The network consists of an input layer, one or more hidden layers, and an output layer. During a forward pass, data flows from left to right through these interconnected layers of neurons.
The first step in a neural network forward pass is calculating the weighted sum for each neuron. For each neuron, we multiply each input value by its corresponding weight, sum these products, and add a bias term. Mathematically, this is represented as z equals the sum of w_i times x_i plus b, where w_i is the weight for input i, x_i is the input value, and b is the bias term.
The second step in a forward pass is applying an activation function to the weighted sum. This introduces non-linearity, allowing the network to learn complex patterns. Common activation functions include ReLU, which outputs the maximum of zero and the input; Sigmoid, which maps inputs to values between 0 and 1; and Tanh, which maps inputs to values between -1 and 1. The output of the activation function becomes the input for the next layer.
Let's put everything together to understand the complete forward pass process. First, input data X enters the network through the input layer. Then, for each hidden layer, we calculate the weighted sum z and apply an activation function to get a. This process repeats through all hidden layers. Finally, the output layer produces the prediction Y-hat, which could be class probabilities for classification tasks or predicted values for regression tasks. The entire process flows from left to right, with each layer building upon the computations of the previous layer.
To summarize what we've learned about neural network forward pass: First, it's the process that transforms input data into predictions by passing information through interconnected layers. Second, each neuron computes a weighted sum of inputs plus a bias term. Third, activation functions introduce non-linearity, which enables the network to learn complex patterns. Fourth, data flows sequentially through the network from input to output layers. Finally, the output format depends on the specific task, such as classification probabilities or regression values.