Most modern deep learning models are based on multi-layered neural networks such as
Fundamentally, deep … See more

Deep neural networks are generally interpreted in terms of the universal approximation theorem or probabilistic inference.
The classic universal approximation theorem co… See more

There were two types of artificial neural network (ANN): feedforward neural networks (FNNs) and recurrent neural networks (RNNs). RNNs have cycles in their connectivity structure, FNNs don't. In the 1920s, … See more

Artificial neural networks (ANNs) or connectionist systems are computing systems inspired by the biological neural networks that constitute animal brains. Such systems learn (progressively improve their ability) to d… See more

Since the 2010s, advances in both machine learning algorithms and computer hardware have led to more efficient methods for training deep neural networks that contain many layers of non-linear hidden units and a very larg… See more

Large-scale automatic speech recognition is the first and most convincing successful case of deep learning. LSTM RNNs can learn "Very Deep Learning" tasks that involve multi-second intervals containing speech eve… See more