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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 explained using Matrices: Part 1

Towards Data Science

Often times we get consumed with using Deep learning frameworks that perform all of the required operations needed to build our models. However, there is some value to first understanding some of the…...

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…

Deep Dive into Gated Recurrent Units (GRU): Understanding the Math behind RNNs

Towards AI

Gated Recurrent Unit (GRU) is a simplified version of Long Short-Term Memory (LSTM). Let’s see how it works in this article. Photo by Laila Gebhard on Unsplash This article will explain the working o...

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...

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…

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.

LSTM And GRU In Depth

Python in Plain English

Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) are used to handle longer and more complex stories without forgetting the important bits. Long Short-Term Memory (LSTM) * A type of RNN des...

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...

Recurrent Neural Network Implementation from Scratch

Dive intro Deep Learning Book

We are now ready to implement an RNN from scratch. In particular, we will train this RNN to function as a character-level language model (see Section 9.4 ) and train it on a corpus consisting of the e...

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

LSTM

PyTorch documentation

Applies a multi-layer long short-term memory (LSTM) RNN to an input sequence. For each element in the input sequence, each layer computes the following function: where h t h_t h t is the hidden stat...

The Recurrent Artificial Neuron

Analytics Vidhya

The previous article in the Introduction to Artificial Neural Network series explained the Recurrent Neural Network (RNN) which constitutes any translation application that we use today. Today, we…

Backpropagation Through Time — How RNNs Learn

Towards Data Science

Recurrent Neural Networks (RNNs) are regular feedforward neural network variants that handle sequence-based data like time series and natural language. They achieve this by adding a “recurrent” neuron...

Recurrent Neural Network

Towards Data Science

In my last blog about NLP I had taken topics of Bag of Words, tokenization, TF-IDF, Word2Vec, all of these had a problem like they don’t store the information of semantics. It is important…

Building A Recurrent Neural Network From Scratch In Python

Towards AI

How to build a basic RNN using Basic Python libraries Continue reading on Towards AI

Recurrent Neural Network-Head to Toe

Towards Data Science

The neurone is a building block of the human brain. It analyses complex signals within microseconds and sends signals to the nervous system to perform tasks. The architecture of the neurone is the…

Modern Recurrent Neural Networks

Dive intro Deep Learning Book

The previous chapter introduced the key ideas behind recurrent neural networks (RNNs). However, just as with convolutional neural networks, there has been a tremendous amount of innovation in RNN arch...

Visualizing memorization in RNNs

Distill

Memorization in Recurrent Neural Networks (RNNs) continues to pose a challenge in many applications. We’d like RNNs to be able to store information over many timesteps and retrieve it when it becomes ...

A Brief Introduction to Recurrent Neural Networks

Towards Data Science

An introduction to RNN, LSTM, and GRU and their implementation RNN, LSTM, and GRU cells. If you want to make predictions on sequential or time series data (e.g., text, audio, etc.) traditional neural...

Concise Implementation of Recurrent Neural Networks

Dive intro Deep Learning Book

Like most of our from-scratch implementations, Section 9.5 was designed to provide insight into how each component works. But when you’re using RNNs every day or writing production code, you will want...

An Intuitive Approach to Understading of LSTMs and GRUs

Towards Data Science

A Recipe for Understanding LSTMs & GRUs We will proceed to prove that LSTMs & GRU are easier than you thought Although RNNs might be what first cross your mind when you hear about natural language pr...

Introduction to LLMs: The RNN Encoder-Decoder Architecture

The AiEdge Newsletter

The sequence-to-sequence models THE RNN encoder-decoder architecture: concepts Implementing in PyTorch Implementing the encoder Implementing the decoder Putting the encoder and decoder together