Deep learning is a subset of artificial intelligence and machine learning inspired by the human brain’s structure and functioning. It involves using neural networks – an algorithm composed of multiple layers of interconnected nodes – to learn and extract meaningful features from complex and large-scale data sets.
Deep learning algorithms are efficient for tasks requiring a high level of abstraction and pattern recognition, such as image and speech recognition, natural language processing, and autonomous driving. They can also be used for predictive modeling, classification, and anomaly detection.
Deep learning has become increasingly popular in recent years because of the availability of large amounts of data and the increased computing power of modern GPUs. Deep learning models can require large amounts of training data and computational resources to achieve high levels of accuracy and performance.
There are various types of neural networks used in deep learning, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and deep belief networks (DBNs). Each type of network has its own strengths and weaknesses and is suitable for a variety of tasks.
Deep learning has been used in a wide range of applications, including autonomous vehicles, natural language processing, drug discovery, fraud detection, and recommender systems. Its ability to learn complex patterns and relationships in data has made it a powerful tool in many areas of research and industry.