Deep learning is a subset of machine learning (ML) that is based on artificial neural networks (ANNs) inspired by the structure and function of the human brain. It involves training algorithms to recognize patterns and make predictions by processing large amounts of data.

Deep learning is characterized by the use of deep neural networks that consist of multiple layers of interconnected nodes. Each node in the network performs a simple mathematical operation on its input and passes the result on to the next layer of nodes. By stacking many layers on top of each other, deep neural networks can learn increasingly complex features and representations of the data.

To train a deep neural network, large amounts of labeled data are needed. The network is fed the input data, and the weights of the connections between nodes are adjusted during training to minimize the difference between the network’s output and the desired output. This process, known as backpropagation, iteratively improves the network’s ability to make accurate predictions.

Deep learning has been successfully applied to a wide range of applications, including image and speech recognition, natural language processing, and autonomous vehicles. It has achieved state-of-the-art results in many tasks and has revolutionized the field of artificial intelligence.

In conclusion, deep learning is a subset of machine learning that uses artificial neural networks with multiple layers to recognize patterns and make predictions. It requires large amounts of labeled data for training and has been applied successfully to a variety of applications, achieving state-of-the-art results in many tasks.