Index
1 Introduction to Neural Networks
This section provides an overview of neural networks, explaining their structure and components. It covers the role of neurons, layers, and weights in the network, and introduces the concept of forward propagation.
2 Types of Neural Network Layers
In this chapter, you will learn about different types of layers in a neural network. It covers input layers, hidden layers, and output layers, and explains their functions and connections within the network.
3 Activation Functions in Deep Learning
Activation functions play a crucial role in neural networks. This section explores various activation functions, such as sigmoid, ReLU, and tanh, and discusses their properties and applications in deep learning.
4 Understanding Backpropagation
Backpropagation is a key algorithm for training neural networks. This chapter explains the concept of backpropagation, including how it calculates gradients and updates the weights of the network.
5 Popular Neural Network Architectures
This section introduces popular neural network architectures, such as feedforward neural networks, convolutional neural networks (CNNs), and recurrent neural networks (RNNs). It discusses their unique characteristics and applications.
6 Optimization Techniques for Neural Networks
Optimizing neural networks is essential for achieving better performance. This chapter covers various optimization techniques, including gradient descent, stochastic gradient descent (SGD), and adaptive learning rate methods like Adam. It also discusses regularization techniques like dropout and batch normalization.