What is deep learning?

Deep Learning is a subset of machine learning, which in turn is a branch of artificial intelligence that aims to emulate the learning approach that humans use to gain certain types of knowledge. At its core, deep learning involves training computer systems on a large amount of data using algorithms modeled after the structure and function of the human brain, known specifically as artificial neural networks.

Deep learning techniques enable the computer to learn from the data by automatically extracting features and performing tasks such as classification, prediction, decision-making, and voice and image recognition without being explicitly programmed for the task at hand. The “deep” in deep learning refers to the use of multiple layers in the network—each layer processes an aspect of the data, and the output of one layer becomes the input for the next. This depth allows the network to learn complex patterns in large amounts of data.

Deep learning applications are vast and include fields like autonomous vehicles, where they enable decision-making in real-time; natural language processing, for tasks such as translating text between languages or understanding human speech; and computer vision, which allows computers to interpret and understand the visual world.

The primary advantage of deep learning is its ability to perform feature extraction automatically without human intervention, unlike traditional machine learning algorithms where features need to be manually specified. However, deep learning models require large amounts of labeled data and significant computational power to train, which can be a limitation for some applications.