This repository provides an implementation of a neural network from scratch in Python. It includes an explanation of how neural networks work, with a focus on forward propagation and backpropagation.
A neural network is a machine learning model inspired by the human brain. It consists of interconnected layers of artificial neurons called nodes or units. These nodes process input data and generate output predictions.
Forward propagation is the process of passing input data through the neural network to obtain predictions. It involves the following steps:
-
Input Layer: The input data is fed into the neural network.
-
Hidden Layers: The input data is transformed and processed by a series of hidden layers. Each hidden layer applies a weighted sum of the inputs and passes it through an activation function.
-
Output Layer: The final hidden layer's outputs are passed to the output layer, which produces the predicted values.
Backpropagation is the process of updating the weights of the neural network based on the error between the predicted output and the actual output. It involves the following steps:
-
Loss Calculation: The error between the predicted output and the actual output is calculated using a loss function, such as mean squared error or cross-entropy loss.
-
Gradient Calculation: The gradients of the loss function with respect to the network's weights are computed using the chain rule of calculus.
-
Weight Update: The weights are updated using an optimization algorithm, such as gradient descent, by moving in the opposite direction of the gradients to minimize the loss.
-
Clone the repository:
git clone https://github.com/zillur-av/neural-network.git
-
Run
neural-network.ipynb