Machine Learning (ML) and Deep Learning (DL) are both subfields of Artificial Intelligence (AI) that involve training algorithms to perform tasks without explicitly programming them. While they share similarities, they also have distinct characteristics and applications.
Machine learning is a broader field that encompasses various techniques and methods for enabling computers to learn from data and improve their performance on specific tasks over time. It involves training algorithms to identify patterns, make predictions, or take actions based on input data. ML algorithms are designed to adapt and improve as they are exposed to more data.
Machine learning techniques include:
Deep learning is a subset of machine learning that focuses on artificial neural networks with multiple layers (deep neural networks). These networks, inspired by the structure of the human brain, learn hierarchical representations of data by automatically extracting features at multiple levels of abstraction. Deep learning has gained prominence due to its ability to automatically learn complex patterns from large datasets.
Deep learning techniques include:
In summary, deep learning is a subset of machine learning that specializes in neural networks with multiple layers, enabling it to model intricate patterns and hierarchies. Machine learning is a broader field that encompasses various techniques for training algorithms to perform tasks based on data.
Both machine learning and deep learning have found applications in a wide range of fields, including image recognition, natural language processing, robotics, recommendation systems, and more.