Neural networks differ from traditional machine learning (ML) models in their architecture, flexibility, and use cases. Traditional ML models, such as linear regression, decision trees, or support vector machines (SVMs), rely on explicit feature engineering and mathematical formulations to map inputs to outputs. For example, a linear regression model uses weighted sums of input features to predict a target variable, while a decision tree splits data based on rules derived from feature thresholds. These models are often simpler to train and interpret but struggle with complex, high-dimensional data like images or text. Neural networks, on the other hand, use interconnected layers of artificial neurons to automatically learn hierarchical representations of data. This allows them to handle unstructured data and discover patterns without heavy manual feature engineering.
A key distinction is how neural networks handle non-linear relationships. While SVMs or decision trees can model some non-linearity through kernels or splits, neural networks excel at capturing intricate patterns via activation functions (e.g., ReLU) and deep architectures. For instance, convolutional neural networks (CNNs) automatically detect edges, textures, and shapes in images by applying filters across spatial dimensions—a task impractical for models like logistic regression. Similarly, recurrent neural networks (RNNs) process sequential data by maintaining internal memory, making them suitable for tasks like language translation. Traditional models lack this adaptability, often requiring manual tuning or domain-specific preprocessing to achieve comparable results on such tasks.
Another difference lies in scalability and computational demands. Neural networks typically require large datasets and significant computational resources (e.g., GPUs) to train effectively, whereas traditional models like random forests or k-nearest neighbors (KNN) can work well with smaller datasets and less hardware. For example, training a ResNet model on millions of images might take hours on a GPU cluster, while a random forest for predicting customer churn could be trained on a laptop in minutes. However, neural networks often outperform traditional models on tasks involving raw, unstructured data. Developers might choose a simpler model like a linear regression for interpretable, tabular data analysis but opt for a neural network when dealing with complex inputs like audio, video, or natural language, where automated feature extraction is critical.