Training a neural network involves the process of optimizing its parameters to achieve a desired output. This is done through forward and backward propagation, cost functions, and gradient descent.

Forward Propagation: In forward propagation, the input data is multiplied by the weights and passed through activation functions in each neuron. This helps to calculate the output of the neural network.

Backward Propagation: Backward propagation is where the error is calculated by comparing the actual output with the desired output. The error is then propagated back through the network, adjusting the weights based on their impact on the error.

Cost Functions: Cost functions measure how well the neural network is performing by quantifying the difference between predicted and actual outputs. Examples include mean squared error (MSE) and cross-entropy loss.

Gradient Descent: Gradient descent is a mathematical optimization algorithm used to minimize the cost function by iteratively adjusting the weights to reach the global minimum.

By iteratively adjusting the weights based on the error, a neural network gradually improves its performance and learns to make accurate predictions.

Remember, practice makes perfect in training neural networks! Keep experimenting with different architectures and hyperparameter settings to unleash your network's true potential.