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.
Have you ever wondered how predictive text algorithm works? How exactly does that speech recognition software know our voice? As for image classification, convolutional neural networks were turning the whiles behind the scene, for these kinds of problems we are using Recurrent Neural Networks (RNN). These Neural Networks are very powerful and they are especially useful in so-called Natural Language Processing (NLP). One might wonder what makes them so special.Well, the networks we examined so far, Standard Neural Networks and Convolutional Neural Networks, are accepting a fixed-size vector as input and produce a fixed-sized vector as an output.