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## What is LSTM in RNN?

Long short-term memory (LSTM) is an artificial recurrent neural network (RNN) architecture used in the field of deep learning. LSTM networks are well-suited to classifying, processing and making predictions based on time series data, since there can be lags of unknown duration between important events in a time series.

**What is DRNN?**

Abstract: A new neural paradigm called diagonal recurrent neural network (DRNN) is presented. The architecture of DRNN is a modified model of the fully connected recurrent neural network with one hidden layer, and the hidden layer comprises self-recurrent neurons.

**Why Tanh is used in RNN?**

Tanh is used to squish all values between -1, and 1. Along with being useful to mitigate the vanishing/exploding gradient problem as others have stated, it also outputs both negative and positive values.

### What are RNNs used for?

Recurrent neural networks (RNN) are the state of the art algorithm for sequential data and are used by Apple’s Siri and and Google’s voice search. It is the first algorithm that remembers its input, due to an internal memory, which makes it perfectly suited for machine learning problems that involve sequential data.

**Is CNN better than RNN?**

CNN is considered to be more powerful than RNN. RNN includes less feature compatibility when compared to CNN. This CNN takes inputs of fixed sizes and generates fixed size outputs. RNN can handle arbitrary input/output lengths.

**Is ReLU used in RNN?**

For solving the problem of vanishing gradients in feedforward neural networks, ReLU activation function can be used. When we talk about solving the vanishing gradient problem in RNN, we use a more complex architecture (e.g. LSTM). In both of these, activation function is tanh.

## Why ReLU is not used in RNN?

Understanding the source of RNN’s “gradient explosion” should help you understand why ReLU is not recommended. At first sight, ReLUs seem inappropriate for RNNs because they can have very large outputs, so they might be expected to be far more likely to explode than units that have bounded values.

**Why is CNN not RNN?**

A CNN has a different architecture from an RNN. CNNs are “feed-forward neural networks” that use filters and pooling layers, whereas RNNs feed results back into the network (more on this point below). In CNNs, the size of the input and the resulting output are fixed.

**Is CNN faster than LSTM?**

Since CNNs run one order of magnitude faster than both types of LSTM, their use is preferable. All models are robust with respect to their hyperparameters and achieve their maximal predictive power early on in the cases, usually after only a few events, making them highly suitable for runtime predictions.

### What is the definition of a recurrent neural network?

What are recurrent neural networks? A recurrent neural network (RNN) is a type of artificial neural network which uses sequential data or time series data.

**How are recurrent neural networks used to determine gradients?**

Recurrent neural networks leverage backpropagation through time (BPTT) algorithm to determine the gradients, which is slightly different from traditional backpropagation as it is specific to sequence data.

**How are recurrent neural networks used in deep learning?**

A recurrent neural network (RNN) is a type of artificial neural network which uses sequential data or time series data. These deep learning algorithms are commonly used for ordinal or temporal problems, such as language translation, natural language processing (nlp), speech recognition, and image captioning; they are incorporated into popular

## How are weights adjusted in recurrent neural networks?

That said, these weights are still adjusted in the through the processes of backpropagation and gradient descent to facilitate reinforcement learning.