rnn - OpenGenus IQ: Learn Algorithms, DL, System Design

Back-propagation Through Time (BPTT) [Explained]

Back-propagation is the most widely used algorithm to train feed forward neural networks. The generalization of this algorithm to recurrent neural networks is called Back-propagation Through Time (BPTT).

Machine Learning (ML)

Recurrent Neural Network (RNN) questions [with answers]

Practice multiple choice questions on Recurrent Neural Network (RNN) with answers. It is an important Machine Learning model and is a significant alternative to Convolution Neural Network (CNN).

Machine Learning (ML)

Disadvantages of RNN

We have explored the disadvantages of RNN in depth. Recurrent Neural Networks (or RNNs) are the first of their kind neural networks that can help in analyzing and learning sequences of data rather than just instance-based learning.

Machine Learning (ML)

Understanding Recurrent Neural Networks with an example of assigning an emoji to a sentence

In this article, we explored the basic ideas of Recurrent Neural Networks with an example to assign an emoji to a sentence based on the emotion

Machine Learning (ML)

Long Short Term Memory (LSTM)

Long short-term memory (LSTM) units are units of a recurrent neural network (RNN). An RNN composed of LSTM units is often called an LSTM network. A common LSTM unit is composed of a cell, an input gate, an output gate and a forget gate. It has applications in Speech recognition, Video synthesis

Machine Learning (ML)

Recurrent Neural Networks (RNN)

Recurrent Neural Network is one of the widely used algorithms of Deep Learning mainly due to is unique Design. It is the only algorithm that remembers the most recent Input and makes use of memory element. It is used by Apple Siri and Google Voice Search. RNN is used for sequential data.

Machine Learning (ML)

When to use Recurrent Neural Networks (RNN)?

Recurrent Neural Networks (RNNs) are designed to work with sequence prediction problems. RNNs can be used on Text data, Speech data, Classification prediction problems, Regression prediction problems and Generative models. Sequence prediction problems come in many forms.