## Why are LSTMs better than RNNs for sequences?

The main difference between RNN and LSTM is in terms of which one maintain information in the memory for the long period of time. Here LSTM has advantage over RNN as LSTM can handle the information in memory for the long period of time as compare to RNN.

**What are the problems with LSTM?**

In short, LSTM require 4 linear layer (MLP layer) per cell to run at and for each sequence time-step. Linear layers require large amounts of memory bandwidth to be computed, in fact they cannot use many compute unit often because the system has not enough memory bandwidth to feed the computational units.

**What is one of the most common problem with training RNNs and LSTMs?**

The key to the LSTM solution to the technical problems was the specific internal structure of the units used in the model. … governed by its ability to deal with vanishing and exploding gradients, the most common challenge in designing and training RNNs.

### What was the major drawback of RNN?

Disadvantages of Recurrent Neural Network Gradient vanishing and exploding problems. Training an RNN is a very difficult task. It cannot process very long sequences if using tanh or relu as an activation function.

**How many steps can LSTM remember?**

A reasonable limit of 250-500 time steps is often used in practice with large LSTM models.

**When to use a LSTM in a problem?**

LSTMs work very well if your problem has one output for every input, like time series forecasting or text translation. But LSTMs can be challenging to use when you have very long input sequences and only one or a handful of outputs. This is often called sequence labeling, or sequence classification.

## When to use LSTMs for long input sequences?

But LSTMs can be challenging to use when you have very long input sequences and only one or a handful of outputs. This is often called sequence labeling, or sequence classification. Classification of sentiment in documents containing thousands of words (natural language processing).

**How can LSTMs speed up the learning process?**

Rather than updating the model based on the entire sequence, the gradient can be estimated from a subset of the last time steps. This is called Truncated Backpropagation Through Time, or TBPTT for short. It can dramatically speed up the learning process of recurrent neural networks like LSTMs on long sequences.

**How to train hidden state in LSTM and RNN?**

Instead of RNN we will first try to train this in a simple multi layer neural network with one input and one output, here hidden layers details doesn’t matter. where x is an element of X and y is an element of Y and f () is our neural network. Now instead of the above sequence try teach this sequence to the same neural network.