How does deep learning differ from machine learning?

Deep learning is a specialized subset of machine learning that focuses on neural networks with multiple layers, enabling it to automatically learn hierarchical representations of data. While both fields involve training algorithms to recognize patterns, deep learning distinguishes itself through its reliance on deep neural networks—architectures with many interconnected layers. Traditional machine learning often relies on handcrafted features and simpler models like decision trees, support vector machines (SVMs), or linear regression. In contrast, deep learning models, such as convolutional neural networks (CNNs) or transformers, automatically extract features from raw data, reducing the need for manual feature engineering. For example, in image recognition, a traditional machine learning model might require manually identifying edges or textures, while a CNN learns these features directly from pixel data.

A key difference lies in how these approaches handle data complexity and scalability. Machine learning models typically perform well on structured, tabular data with clear features, such as predicting housing prices using square footage or location. Deep learning, however, excels with unstructured data like images, audio, or text, where relationships between inputs are less obvious. For instance, training a recurrent neural network (RNN) to generate text involves processing sequences of words, while a traditional model like a random forest might struggle with such tasks. Deep learning also demands significantly more computational resources and data. Training a large neural network often requires GPUs and vast datasets, whereas simpler machine learning algorithms can run efficiently on CPUs with smaller datasets.

Another distinction is interpretability and use cases. Machine learning models are generally easier to debug and explain. For example, a decision tree’s rules can be visualized, making it suitable for applications like credit scoring where transparency matters. Deep learning models, by contrast, operate as “black boxes,” making them less ideal for scenarios requiring accountability. However, their ability to handle complexity makes them dominant in areas like computer vision (e.g., self-driving cars detecting pedestrians) or natural language processing (e.g., chatbots understanding context). Developers choosing between the two often weigh factors like data size, problem complexity, and resource availability—opting for machine learning when simplicity and speed are priorities, and deep learning for tasks requiring nuanced pattern recognition in unstructured data.