In this article, I will explain how to perform Training Neural Networks Using Backpropagation.

Backpropagation is the primary algorithm used to train neural networks. It is a supervised learning algorithm that involves iteratively adjusting the weights of a neural network to minimize the difference between the predicted output and the target output. Here are the basic steps involved in training a neural network using backpropagation:

- Initialization: Initialize the weights of the neural network with small random values.
- Forward propagation: Feed the input data through the network and compute the output. This is done by applying a series of matrix multiplications and activation functions.
- Loss calculation: Compare the predicted output to the target output and compute the loss. The loss function used depends on the type of problem being solved.
- Backward propagation: Compute the gradient of the loss with respect to the weights of the network using the chain rule of calculus. This is done by propagating the error back through the network from the output layer to the input layer.
- Weight update: Adjust the weights of the network in the direction that reduces the loss. This is done using an optimization algorithm such as stochastic gradient descent or Adam.
- Repeat steps 2-5: Repeat the process of forward propagation, loss calculation, backward propagation, and weight update for a specified number of iterations or until the loss converges to a satisfactory value.

By following these steps, you can train a neural network to accurately predict outputs for a given input. However, it is important to choose appropriate hyperparameters such as learning rate, batch size, and number of hidden layers to ensure that the network is trained effectively and efficiently. Additionally, regularization techniques such as dropout and weight decay can be used to prevent overfitting and improve generalization performance.

**Further Reading**

Deep Learning Practice Exercise

Deep Learning Methods for Object Detection