CandleFocus Glossary Editor
Deep learning is an artificial intelligence area that has gained a lot of popularity due to its successful applications in many different areas. Deep learning is a subfield of machine learning that uses algorithms inspired by the structure and function of the brain's neural networks to analyze and recognize patterns. It can detect complex relationships and patterns between large volumes of data and can be used to predict future outcomes.
Deep learning is based on the concept of artificial neural networks that simulate a version of the human brain’s neural networks. These artificial neural networks are made up of layers of interconnected nodes that process and analyze data in order to identify patterns and make decisions. A neural network is initiated with a set of inputs in an attempt to provide an expected output, then the network is trained to improve performance.
Deep learning can process unstructured data such as text, audio, and video, and is used to power state-of-the-art artificial intelligence services such as facial recognition, image recognition, natural language processing, and automatic speech recognition.
The way deep learning works is by exposing the neural networks to large amounts of data in the form of training examples. For example, if a neural network were being used for facial recognition, it would be trained on a dataset of hundreds of thousands of images from various directions and lighting conditions. This type of data is usually far more than traditional methods can process. Then, as the neural network is exposed to more data, its accuracy improves.
The development of deep learning has been accelerated by the rise of powerful computing systems, such as GPUs and the cloud. These computing systems’ capability to process large amounts of data quickly allows deep learning models to be trained and tested much faster.
In summary, deep learning is a subfield of machine learning used to analyze complex patterns in large datasets. It makes use of artificial neural networks made up of layers of interconnected nodes to process data and can be used to power AI services such as facial recognition, image recognition, and natural language processing. It has been accelerated by powerful computing systems and could be used in a wide range of applications from healthcare to autonomous vehicles.
Deep learning is based on the concept of artificial neural networks that simulate a version of the human brain’s neural networks. These artificial neural networks are made up of layers of interconnected nodes that process and analyze data in order to identify patterns and make decisions. A neural network is initiated with a set of inputs in an attempt to provide an expected output, then the network is trained to improve performance.
Deep learning can process unstructured data such as text, audio, and video, and is used to power state-of-the-art artificial intelligence services such as facial recognition, image recognition, natural language processing, and automatic speech recognition.
The way deep learning works is by exposing the neural networks to large amounts of data in the form of training examples. For example, if a neural network were being used for facial recognition, it would be trained on a dataset of hundreds of thousands of images from various directions and lighting conditions. This type of data is usually far more than traditional methods can process. Then, as the neural network is exposed to more data, its accuracy improves.
The development of deep learning has been accelerated by the rise of powerful computing systems, such as GPUs and the cloud. These computing systems’ capability to process large amounts of data quickly allows deep learning models to be trained and tested much faster.
In summary, deep learning is a subfield of machine learning used to analyze complex patterns in large datasets. It makes use of artificial neural networks made up of layers of interconnected nodes to process data and can be used to power AI services such as facial recognition, image recognition, and natural language processing. It has been accelerated by powerful computing systems and could be used in a wide range of applications from healthcare to autonomous vehicles.