7.1 Introduction to Deep Learning
This section provides an overview of deep learning, including neural networks, activation functions, and backpropagation. It also covers the basics of supervised and unsupervised learning.
7.2 Transfer Learning Fundamentals
This chapter introduces the concept of transfer learning, including the benefits and challenges of using pre-trained models. It also covers the different types of transfer learning and how to choose the right pre-trained model for a new task.
7.3 Fine-tuning Pre-trained Models
In this section, you will learn how to adapt pre-trained models to new tasks through fine-tuning. It covers the different layers of a pre-trained model that can be fine-tuned, as well as the techniques for optimizing the fine-tuning process.
7.4 Applications of Transfer Learning
This chapter explores the various applications of transfer learning, including image classification, natural language processing, and speech recognition. It also covers the limitations of transfer learning and when it may not be appropriate to use.
7.5 Evaluation and Performance Metrics
In this section, you will learn how to evaluate the performance of a transfer learning model using various metrics, such as accuracy, precision, and recall. It also covers the importance of choosing the right evaluation metric for a specific task.
7.6 Future of Transfer Learning
This chapter discusses the current state and future directions of transfer learning research. It covers the latest advancements in transfer learning, such as meta-learning and domain adaptation, and their potential impact on various industries.