Deep Recurrent Network

Machine learning techniques have been widely applied in various areas such as pattern recognition, natural language processing, and computational learning. During the past decades, machine learning has had an enormous influence on our daily lives with examples including efficient web?search, self-driving systems, computer vision,?and optical character recognition (OCR). Especially, deep learning models have become a powerful tool for machine learning and artificial intelligence. A deep neural network (DNN) is an artificial neural network (ANN) with multiple layers between the input and output layers. Note that the terms ANN vs. DNN are often incorrectly confused or used interchangeably. The success of deep learning neural networks has led to breakthroughs such as reducing word error rates in speech recognition by 30% over traditional approaches (the biggest gain in 20 years) or drastically cutting the error rate in an image recognition competition since 2011 (from 26% to 3.5% while humans achieve 5%). Concept of Deep Neural Networks Deep neural network models were originally inspired by neurobiology.

On a high level, a biological neuron receives multiple signals through the synapses contacting its dendrites and sending a single stream of action potentials out through its axon. The complexity of multiple inputs is reduced by categorizing its input patterns. Inspired by this intuition, artificial neural network models are composed of units that combine multiple inputs and produce a single output. Deep Learning Layers explained Neural networks target brain-like functionality and are based on a simple artificial neuron: a nonlinear function (such as max(0, value)) of a weighted sum of the inputs. These pseudo neurons are collected into layers, and the outputs of one layer become the inputs of the next in the sequence. What makes a Neural Network “Deep”? Deep neural networks employ deep architectures in neural networks. “Deep” refers to functions with higher complexity in the number of layers and units in a single layer. The ability to manage large datasets in the cloud made it possible to build more accurate models by using additional and larger layers to capture higher levels of patterns.

The two key phases of neural networks are called training (or learning) and inference (or prediction), and they refer to the development phase versus production or application. When creating the architecture of deep network systems, the developer chooses the number of layers and the type of neural network, and training data determines the weights. ? 3 Types of Deep Neural Networks Three following types of deep neural networks are popularly used today: Multi-Layer Perceptrons (MLP) Convolutional Neural Networks (CNN) Recurrent Neural Networks (RNN) ?