Inductive Bias in DNNs

Deep neural networks (DNNs), inspired by the human brain, form the core of modern machine learning. As we explore inductive bias, and the inherent assumptions guiding network connectivity, we understand how it shapes adaptability and efficiency.

Deep neural networks (DNNs): Foundations of learning

Artificial neural networks, the backbone of modern machine learning, replicate the interconnected nodes and layered structures inspired by the human brain. In these networks, fully connected layers facilitate the exploration of intricate relationships within input data, aiming to create universal function approximators. Stacking these layers allows for the extraction of complex patterns and abstract features, but the lack of prior assumptions about input relationships can lead to inefficiencies, given the risk of over-parameterization. To address this, specialized architectures like convolutional neural networks (CNNs) and recurrent neural networks (RNNs) have emerged, tailoring connectivity patterns to specific data types, such as images or text.

Let's start by exploring the concept of inductive bias or modeling assumptions and how it applies to neural networks as graphs.

Inductive bias

As we transition from the foundations of DNNs to the concept of inductive bias, it becomes clear that the architecture's connectivity profoundly influences learning. Inductive bias refers to the inherent assumptions or modeling choices that guide network connectivity. Whether it's the comprehensive links in fully connected layers or the spatial and sequential relationships in CNNs and RNNs, these biases shape how a network generalizes from data. The trade-off between weak and strong inductive biases reflects the tension between adaptability and efficiency. Understanding this spectrum equips us to appreciate the design choices behind neural networks and sets the stage for exploring how inductive bias impacts performance.