Then you multiply the tanh output with the sigmoid output. The sigmoid output will decide which information is important to keep from the tanh output.Now we should have enough information to calculate the cell state. Then we pass the newly modified cell state to the tanh function. CNNs opened your eyes to the world of … That gives us our new cell state.Last we have the output gate. It holds information on previous data the network has seen before.Let’s look at a cell of the RNN to see how you would calculate the hidden state. The output gate determines what the next hidden state should be.For those of you who understand better through seeing the code, here is an example using python pseudo code.1. If you want to understand what’s happening under the hood for these two networks, then this post is for you.You can also watch the video version of this post on youtube if you prefer.Recurrent Neural Networks suffer from short-term memory. LSTM refresher. Let me guess… You’ve completed a couple little projects with MLPs and CNNs, right? Combining all those mechanisms, an LSTM can choose which information is relevant to remember or forget during sequence processing.So now we know how an LSTM work, let’s briefly look at the GRU. In this case, the words you remembered made you judge that it was good.To understand how LSTM’s or GRU’s achieves this, let’s review the recurrent neural network. You can even use them to generate captions for videos.Ok, so by the end of this post you should have a solid understanding of why LSTM’s and GRU’s are good at processing long sequences. Then I’ll explain the internal mechanisms that allow LSTM’s and GRU’s to perform so well. As the cell state goes on its journey, information get’s added or removed to the cell state via gates. MLPs got you started with understanding gradient descent and and activation functions. You also pass the hidden state and current input into the tanh function to squish values between -1 and 1 to help regulate the network. Hi and welcome to an Illustrated Guide to Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU). It also only has two gates, a reset gate and update gate.The update gate acts similar to the forget and input gate of an LSTM. GRU’s has fewer tensor operations; therefore, they are a little speedier to train then LSTM’s. It processes data passing on information as it propagates forward. It decides what information to throw away and what new information to add.The reset gate is another gate is used to decide how much past information to forget.And that’s a GRU. Second, During backprop through each LSTM cell, it’s multiplied by different values of forget fate, which makes it less prone to vanishing/exploding gradient. You’ll first read the review then determine if someone thought it was good or if it was bad.When you read the review, your brain subconsciously only remembers important keywords. Long Short-Term Memory (LSTM) is an RNN architecture specifically designed to address the vanishing gradient problem. LSTM’s and GRU’s are used in state of the art deep learning applications like speech recognition, speech synthesis, natural language understanding, etc. LSTMs have many variations, but we’ll stick to a simple one. They were introduced by Hochreiter & Schmidhuber (1997) , and were refined and popularized by many people in following work. And I’ll use Aidan’s notation … Let’s say you’re looking at reviews online to determine if you want to buy Life cereal (don’t ask me why). The input gate decides what information is relevant to add from the current step. To solve the problem of Vanishing and Exploding Gradients in a deep Recurrent Neural Network, many variations were developed.