What is a Recurrent Neural Network or RNN, how it works, where it can be used? This article tries to answer the above questions. It also shows a demo implementation of a RNN used for a specific purpose, but you would be able to generalise it for your needs. Python, CNN knowledge is required. CNN is required to compare why and where RNN performs better than CNN? No need to understand the math. If you want to check then go back to my earlier article to check what is a CNN.
Recurrent Networks are one of the most powerful and promising artificial neural network algorithms to processing the sequential data such as natural languages, sound, time series data. Unlike traditional feed-forward network, Recurrent Network has a inherent feed back loop that allows to store the temporal context information and pass the state of information to the entire sequences of the events. This helps to achieve the state of art performance in many important tasks such as language modeling, stock market prediction, image captioning, speech recognition, machine translation and object tracking etc., However, training the fully connected RNN and managing the gradient flow are the complicated process. Many studies are carried out to address the mentioned limitation. This article is intent to provide the brief details about recurrent neurons, its variances and trips & tricks to train the fully recurrent neural network. This review work is carried out as a part of our IPO studio software module 'Multiple Object Tracking'.
Artificial Intelligence, deep learning, machine learning -- whatever you're doing if you don't understand it -- learn it. Because otherwise you're going to be a dinosaur within 3 years. This statement from Mark Cuban might sound drastic – but its message is spot on! We are in middle of a revolution – a revolution caused by Big Huge data and a ton of computational power. For a minute, think how a person would feel in early 20th century if he / she did not understand electricity. You would have been used to doing things in a particular manner for ages and all of a sudden things around you started changing.
Last time, we talked about the traditional feed-forward neural net and concepts that form the basis of deep learning. These ideas are extremely powerful! We saw how feed-forward convolutional neural networks have set records on many difficult tasks including handwritten digit recognition and object classification. And even today, feed-forward neural networks consistently outperform virtually all other approaches to solving classification tasks.
The purpose of this post is to give students of neural networks an intuition about the functioning of recurrent networks and purpose and structure of a prominent variation, LSTMs. Recurrent nets are a type of artificial neural network designed to recognize patterns in sequences of data, such as text, genomes, handwriting, the spoken word, or numerical times series data emanating from sensors, stock markets and government agencies. They are arguably the most powerful type of neural network, applicable even to images, which can be decomposed into a series of patches and treated as a sequence. Since recurrent networks possess a certain type of memory, and memory is also part of the human condition, we'll make repeated analogies to memory in the brain.1 To understand recurrent nets, first you have to understand the basics of feedforward nets. Both of these networks are named after the way they channel information through a series of mathematical operations performed at the nodes of the network.