4.1 Introduction to Deep Learning and Neural Networks
This section provides an overview of deep learning and neural networks, including their applications and how they differ from traditional machine learning algorithms.
4.2 Understanding Convolutional Neural Networks (CNNs)
This chapter explains the architecture of CNNs, including convolutional layers, pooling layers, and fully connected layers. It also covers popular CNN architectures, such as LeNet and AlexNet.
4.3 Preprocessing and Data Augmentation
This section covers the importance of preprocessing and data augmentation in CNNs. You will learn about techniques such as normalization, resizing, and cropping, as well as data augmentation methods like flipping and rotating.
4.4 Training and Optimization
This chapter delves into the training and optimization of CNNs, including loss functions, backpropagation, and gradient descent. You will also learn about popular optimization algorithms like Adam and RMSprop.
4.5 Transfer Learning and Fine-Tuning
Transfer learning and fine-tuning are powerful techniques for leveraging pre-trained CNN models. This section covers how to use pre-trained models for new tasks, as well as how to fine-tune them for improved performance.
4.6 Applications of CNNs
This chapter explores the various applications of CNNs, including image classification, object detection, and facial recognition. You will learn about real-world examples of CNNs in action and how they are changing the field of computer vision.