Deep learning is a subset of machine learning that is concerned with neural networks: how to use backpropagation and certain principles from neuroscience to more accurately model large sets of unlabelled or semi-structured data. In that sense, deep learning represents an unsupervised learning algorithm that learns representations of data through the use of neural nets.
Deep learning is a subset of machine learning that focuses on using neural networks with multiple layers to model and understand complex data representations. Unlike traditional machine learning algorithms that often require handcrafted features and explicit feature engineering, deep learning algorithms automatically learn hierarchical representations of data directly from raw inputs.
Deep learning models, such as deep neural networks (DNNs), convolutional neural networks (CNNs), and recurrent neural networks (RNNs), are capable of automatically discovering intricate patterns and features in data, making them highly effective for tasks like image recognition, natural language processing, and speech recognition.
Contrastingly, other machine learning algorithms typically rely on simpler models, such as decision trees, support vector machines (SVMs), and linear regression, which require feature engineering and selection as a preprocessing step. These traditional algorithms often struggle to capture complex patterns in data without explicit feature engineering and may not scale well to large datasets or high-dimensional inputs.
In summary, while traditional machine learning algorithms rely on handcrafted features and explicit feature engineering, deep learning algorithms automatically learn hierarchical representations of data directly from raw inputs, making them particularly effective for handling complex and high-dimensional data.