## Backward Propagation

After the neural network has made its forward propagation prediction, we can **compare that prediction with the real data** and calculate the network **error**. Backpropagation is the process in which this error information is used to traverse the network back and **adjust the weights** of the neurons. Basically, we tell the network, "Here's where you went wrong, let's fix it." Based on this information, the network error is reduced and it becomes more accurate in its predictions.

The learning process of a neural network is the **repetition** of these two stages (forward and back propagation) many times. With each iteration, the network gets smarter and smarter as it learns more about the data and how to process it to make accurate predictions.

It is important to understand that this process does not end when the network reaches "perfect accuracy" or an ideal state, because such a state does not exist. Instead, training usually stops when the network reaches an acceptable level of accuracy, or when it **stops improving** even after many training iterations.


What is forward propagation in a neural network?

What is backpropagation in a neural network?

When training a neural network, what happens after forward propagation stage?

Neural networks are powerful algorithms inspired by the structure of the human brain that are used to solve complex machine learning problems. You will build your own Neural Network from scratch to understand how it works. After this course, you will be able to create neural networks for solving classification and regression problems using the scikit-learn library.

First, we will discuss what a neural network is and how it works. And also consider the scope of its application.

Next, we will try to build our own neural network and see how efficiently it copes with learning. We will also consider a ready-made solution from the scikit-learn library.

Finally, we will give you some additional useful information on how to understand which model to use and what types of neural networks there are. To complete the course, you will be tested on your acquired knowledge.

Introduction to Neural Networks

Forward and Backward Propagation

Forward Propagation

Backward Propagation

1. What is forward propagation in a neural network?

2. What is backpropagation in a neural network?

3. When training a neural network, what happens after forward propagation stage?