Introduction to Specialized Architectures
Feedforward neural networks, or Multi-Layer Perceptrons (MLPs), provide a strong foundation. However, they are not always the optimal choice for certain data structures, particularly grid-like data (e.g., images) and sequential data (e.g., text or time series). This chapter introduces specialized architectures developed to address these specific data types more effectively.
First, we will examine Convolutional Neural Networks (CNNs). You will learn about their key components, such as convolutional and pooling layers, and understand why these structures are effective for processing spatial information.
Next, we will turn to Recurrent Neural Networks (RNNs). We will discuss how RNNs handle sequential information by incorporating loops and maintaining a hidden state, allowing them to model dependencies over time or position within a sequence. This chapter provides a conceptual understanding of the motivation, structure, and basic operation of CNNs and RNNs.
Sections
7.1 Limitations of Feedforward Networks
7.2 Convolutional Neural Networks (CNNs): Motivation
7.3 Core CNN Operations: Convolution
7.4 Core CNN Operations: Pooling
7.5 Typical CNN Architecture
7.6 Recurrent Neural Networks (RNNs): Motivation
7.7 The Concept of Recurrence and Hidden State
7.8 Basic RNN Architecture
7.9 Challenges with Simple RNNs (Vanishing/Exploding Gradients)
7.10 Conceptual Overview: LSTMs and GRUs
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