Having established what multimodal AI is, how different data types are represented, and the techniques for their integration, we now shift our attention to the actual building blocks of these systems. This chapter examines the common elements that constitute multimodal AI models, providing insight into how they are constructed and assessed.
You will learn about methods for extracting meaningful features from various modalities, including text, image, and audio data. We will then discuss simple neural network layers frequently employed in multimodal tasks, alongside an introduction to loss functions suitable for combined data types. Additionally, we'll provide an overview of the training process for these systems and cover basic metrics used for evaluating their performance. The chapter aims to equip you with an understanding of these core pieces, preparing you for a practical activity where you'll outline a simple multimodal model.
4.1 Extracting Features from Text Data
4.2 Extracting Features from Image Data
4.3 Extracting Features from Audio Data
4.4 Simple Neural Network Layers for Multimodal Tasks
4.5 Measuring Performance: Loss Functions for Combined Data
4.6 Training Multimodal Systems: An Overview
4.7 Basic Evaluation Metrics for Multimodal Outputs
4.8 Hands-on Practical: Conceptualizing a Simple Model
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