## Index

### 3.1 Introduction to Neural Networks

This section provides an overview of neural networks, including their structure and function. It covers the basic concepts of neurons, layers, and activation functions.

### 3.2 Forward Propagation

In this chapter, you will learn about the forward propagation process in neural networks. You will explore how input data is processed through the network to produce an output.

### 3.3 Backpropagation

Backpropagation is a key concept in neural network training. This section covers the process of backpropagation, which involves calculating the error between the predicted output and the actual output, and adjusting the weights of the network accordingly.

### 3.4 Gradient Descent

Gradient descent is an optimization algorithm used in neural network training. This chapter explains the concept of gradient descent and how it is used to minimize the error in the network.

### 3.5 Regularization Techniques

Overfitting is a common problem in neural network training. This section covers techniques for preventing overfitting, such as dropout and L1/L2 regularization.

### 3.6 Optimization Algorithms

There are several optimization algorithms that can be used to improve the training process of neural networks. This chapter explores popular algorithms such as stochastic gradient descent, Adam, and RMSprop.