Gated recurrent unit - Learn Data Science with Travis

Gated-recurrent-unit

Gated Recurrent Units (GRUs) are a type of recurrent neural network (RNN) architecture designed to efficiently process sequential data. Introduced in 2014, GRUs simplify the traditional RNN structure by incorporating two key components: the reset gate and the update gate. These gates help manage the flow of information, allowing the model to retain relevant data over long sequences while mitigating issues like the vanishing gradient problem. GRUs are particularly effective in applications such as natural language processing and time-series analysis, making them a popular choice among practitioners in the field of deep learning.

The Math Behind Gated Recurrent Units

Towards Data Science

Gated Recurrent Units (GRUs) are a powerful type of recurrent neural network (RNN) designed to handle sequential data efficiently. In this article, we’ll explore what GRUs are, and…

Gated Recurrent Units (GRU)

Dive intro Deep Learning Book

As RNNs and particularly the LSTM architecture ( Section 10.1 ) rapidly gained popularity during the 2010s, a number of papers began to experiment with simplified architectures in hopes of retaining t...

Gated Recurrent Units (GRU) — Improving RNNs

Towards Data Science

In this article, I will explore a standard implementation of recurrent neural networks (RNNs): gated recurrent units (GRUs). GRUs were introduced in 2014 by Kyunghyun Cho et al. and are an improvement...

Unlocking Sequential Intelligence: The Power and Efficiency of Gated Recurrent Units in Deep…

Python in Plain English

Unlocking Sequential Intelligence: The Power and Efficiency of Gated Recurrent Units in Deep Learning Abstract Context: Gated Recurrent Units (GRU) has emerged as a formidable architecture within the...

Long Short Term Memory and Gated Recurrent Unit’s Explained — ELI5 Way

Towards Data Science

Hi All, welcome to my blog “Long Short Term Memory and Gated Recurrent Unit’s Explained — ELI5 Way” this is my last blog of the year 2019. My name is Niranjan Kumar and I’m a Senior Consultant Data…

Recurrent Neural Networks — Part 4

Towards Data Science

In this blog post, we introduce the concept of gated recurrent units. Having fewer parameters than the LSTM, yet still empirically yield similar performance.

Understanding Gated Recurrent Neural Networks

Analytics Vidhya

I strongly recommend to first know how a Recurrent Neural Network algorithm works to get along with this post of Gated RNN’s. Before getting into the details, let us first discuss about the need to…

GRU Recurrent Neural Networks — A Smart Way to Predict Sequences in Python

Towards Data Science

A visual explanation of Gated Recurrent Units including an end to end Python example of their use with real-life data Continue reading on Towards Data Science

A deep dive into the world of gated Recurrent Neural Networks: LSTM and GRU

Analytics Vidhya

RNNs can further be improved using the gated RNN architecture. LSTM and GRU are some examples of this. The articles explain both the architectures in detail.

[NIPS 2017/Part 1] Gated Recurrent Convolution NN for OCR with Interactive Code [ Manual Back Prop…

Towards Data Science

So this is the first part of implementing Gated Recurrent Convolutional Neural Network. And I will cover one by one, so for today lets implement a simple Recurrent Convolutional Neural Network as a…

GRUCell

PyTorch documentation

A gated recurrent unit (GRU) cell where σ \sigma σ is the sigmoid function, and ∗ * ∗ is the Hadamard product. input_size ( int ) – The number of expected features in the input x hidden_size ( int ) –...

Gated Recurrent Neural Network from Scratch in Julia

Towards AI

Let’s explore Julia to build RNN with GRU cells from zero Image by Author. 1\. Introduction Some time ago, I started learning Julia for scientific programming and data science. The continued adoption...