What is deep learning, and how does it differ from traditional machine learning algorithms?
What is deep learning, and how does it differ from traditional machine learning algorithms?
Ask by Utpal Vishwas
Updated 21 Apr 2023
Deep learning is a subfield of machine learning that involves training neural networks with multiple layers to learn and make predictions from data. It is based on the idea that a computer can learn to recognize patterns and features in data, much like the human brain does.
Traditional machine learning algorithms are typically designed to solve specific problems, such as classification or regression, by learning a set of rules or patterns from labelled examples. These algorithms often rely on feature engineering, which involves manually selecting and extracting relevant features from the input data. For example, if we want to classify images of cats and dogs, we might manually select features such as the size, shape, and colour of the animal.
Deep learning algorithms, on the other hand, can automatically learn and extract relevant features from raw data. By using neural networks with multiple layers, these algorithms can learn increasingly complex representations of the input data, allowing them to solve more complex problems. For example, a deep learning algorithm could learn to recognize a cat based on the pixels in an image, without requiring any manual feature engineering.
Another important difference between deep learning and traditional machine learning is the amount of data required for training. Deep learning algorithms typically require large amounts of labelled data to achieve high levels of accuracy, while traditional machine learning algorithms can often perform well with smaller datasets.
Deep learning is used in many areas of machine learning and artificial intelligence, such as computer vision, natural language processing and speech recognition.