What is Recurrent Neural Network (RNN)
/ February 20, 2018

Neural networks are powerful learning models that achieve state-of-the-art results in a wide range of supervised and unsupervised machine learning tasks. They are suited especially well for machine perception tasks, where the raw underlying features are not individually interpretable. The use of recurrent neural networks are often related to deep learning and the use of sequences to evolve models that simulate the neural activity in the human brain. Overview of Recurrent Neural Network (RNN) The fundamental feature of a Recurrent Neural Network (RNN) is that the network contains at least one feed-back connection, so the activations can flow round in a loop. That enables the networks to do temporal processing and learn sequences, e.g., perform sequence recognition/reproduction or temporal association/prediction. Recurrent Neural Networks (RNNs) are connectionist models with the ability to selectively pass information across sequence steps, while processing sequential data one element at a time. Thus they can model input and/or output consisting of sequences of elements that are not independent. Further, recurrent neural networks can simultaneously model sequential and time dependencies on multiple scales. Figure 1: Recurrent Neural Network In other words, the RNN will be a function with inputs​$$x_t$$​  (input vector) and previous state ​\( h_(t-1)…

Neural Network
/ August 31, 2017

Neural Network Basics An Artificial Neural Network (ANN) is an information processing paradigm that is inspired by the way biological nervous systems, such as the brain, process information. The key element of this paradigm is the novel structure of the information processing system. It is composed of a large number of highly interconnected processing elements (neurons) working in unison to solve specific problems. ANNs, like people, learn by example. An ANN is configured for a specific application, such as pattern recognition or data classification, through a learning process. Learning in biological systems involves adjustments to the synaptic connections that exist between the neurons. This is true of ANNs as well. Neural Network  Definition Work on artificial neural networks, commonly referred to as “neural networks,” has been motivated right from its inception by the recognition that the human brain computes in an entirely different way from the conventional digital computer. The brain is a highly complex, nonlinear, and parallel computer (information-processing system). It has the capability to organize its structural constituents, known as neurons, so as to perform certain computations (e.g., pattern recognition, perception, and motor control) many times faster than the fastest digital computer in existence today. Consider, for example,…