Implementing a Simple Neural Network from Scratch

Building a model that can recognise patterns in data using layers of neurons—each layer converting the input to roughly the intended output—is the first step in implementing a basic neural network from scratch. In order to modify the weights and biases in each layer according to the discrepancy between the expected and actual outputs, this procedure usually entails creating forward and backward passes.

Important Procedures for Setting Up a Basic Neural Network
Describe the architecture: Choose how many input, hidden, and output layers there are, as well as how many neurones there are in each layer. An input layer, one or two hidden layers, and an output layer are typically found in a basic neural network. A simple neural network for binary classification, for example, might contain a hidden layer with a few features and an input layer that matches the feature count.

Forward Propagation: To calculate the expected output, inputs are passed layer by layer via the network. In order to introduce non-linearity and allow the network to learn intricate patterns, each neurone calculates a weighted sum of inputs plus a bias, which is then transformed by an activation function (such as sigmoid or ReLU).

Compute Loss: Use a loss function (such as Mean Squared Error for regression or Cross-Entropy Loss for classification) to determine the difference between the expected and actual output. This loss measures the deviation between the goal values and the predictions.

Backpropagation: By moving the mistake backward through the network, calculate the gradients of the loss with regard to each weight and bias. In order to reduce the error in subsequent forecasts, this gradient information aids in adjusting weights and biases.

Revise Weights and