Deep learning refers to several methods which may be used in a particular application. These methods include convolutional layers and pooling. Simpler and faster activation functions, such as ReLU, return the neuron's weighted sum if it's positive and zero if negative. Regularization techniques, such as dropout, randomly ignore weights during the weight update base to prevent overfitting. GPUs are used for faster training with the order that is 50 times faster. This is because they're optimized for matrix calculations that are used extensively in neural networks and memory units for applications such as speech recognition.
Several factors have contributed to deep learning's dramatic growth in the last five years. Large public datasets, such as ImageNet, that holds millions of labeled images covering a thousand categories and Mozilla's Common Voice Project, that contain speech samples are now available. Such datasets have satisfied the basic requirement for deep learning...