Deep Learning

Definition of Deep Learning

Deep learning is a type of machine learning that uses multiple layers of nonlinear processing units, called neurons, to learn representations of data. Deep learning architectures can learn to represent data in ways that are more accurate and efficient than shallow architectures.

What is Deep Learning used for?

Deep learning is a subfield of machine learning that uses artificial neural networks and algorithms to process, analyze, and interpret large amounts of data. Deep learning models are used for tasks such as image recognition, natural language processing, object detection and tracking, speech recognition, forecasting and prediction, recommendation systems, anomaly detection, automated decision-making and more. Deep learning has enabled a broad range of applications such as self-driving cars, healthcare diagnostics and treatments, natural language understanding (NLU), robotics automation, facial recognition technology, fraud detection systems in financial services industry, augmented reality applications and many more.

Deep learning models are based on the principles of artificial neural networks (ANNs) which simulate the way humans learn by making connections between input data examples and output predictions. ANNs are complex mathematical functions consisting of multiple layers of neurons or nodes organized into interconnected pathways known as networks. Each layer processes its own set of inputs from the previous layer until all layers have been processed. A deep neural network consists of multiple such layers stacked on top of each other so that the model can learn abstract representations from a given set of inputs. By varying the structure and parameters in different deep learning architectures such as convolutional neural networks (CNNs) or recurrent neural networks (RNNs), deep learning models can be tailored to perform specific tasks with unparalleled accuracy for complex problems involving massive amounts of data. Deep learning is currently revolutionizing industries such as healthcare, finance, security operations and more due to its capability to uncover patterns from extremely large datasets that would otherwise be too challenging for traditional machine learning techniques.