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## Can a multi state LSTM be used for all categorical features?

For the sake of our previous example we considered the “user_id” categorical feature, but this can actually be applied to all of our categorical features. In fact, using a Multi-state LSTM on the “ exercise_id ” feature for example would result in it learning historical success rates for each exercise.

**Is the baseline model the same as the LSTM?**

Both classifiers have the same approach. In fact, the baseline model uses handmade moving averages as features and the LSTM-based method automatically learns these histories to predict the scores. Sure enough, both algorithms got similar Accuracy Scores (Baseline : 74.61%, LSTM-based network : 75.34% ).

**Which is the best type of LSTM for machine translation?**

Among all these architectures, Long Short Term Memory ( LSTM) — a particular case of Recurrent Neural Networks — have proven very successful on tasks such as machine translation, time series prediction or generally anything where the data is sequential.

### How is meta learning used to train MAML?

Using meta-learning, one can formulate and train systems that can very quickly learn from a small training set (i.e. a support set), containing only 1-5 samples from each class, such that it can generalize strongly on a corresponding small validation set (i.e. a target set).

**How to predict the future using LSTM networks?**

Predicting the future of sequential data like stocks using Long Short Term Memory (LSTM) networks. Forecasting is the process of predicting the future using current and previous data. The major challenge is understanding the patterns in the sequence of data and then using this pattern to analyse the future.

**How is a label fed to a multi state LSTM?**

Every time a student’s label is fed to the Multi-state LSTM cell, it starts by looking up the corresponding state and previous score. Then, the student’s state is sent to update a shared LSTM cell. In meantime, the previous score is fed to this LSTM cell which produces an output.

## How does a multi state LSTM update a score?

Then, the student’s state is sent to update a shared LSTM cell. In meantime, the previous score is fed to this LSTM cell which produces an output. Next, the resulting state goes back to update the student’s data in the Multi-state.

**Which is the default representation in a LSTM?**

Let’s dive into the experiments. We will perform 5 experiments; each will use a different number of lag observations as features from 1 to 5. A representation with a 1 input feature would be the default representation when using a stateful LSTM.

**How to use features in LSTM networks for time series?**

A representation with a 1 input feature would be the default representation when using a stateful LSTM. Using 2 to 5 features is contrived. The hope would be that the additional context from the lagged observations may improve performance of the predictive model.

### Can a LSTM be used in any other domain?

This concept is usable for any other domain, where sequence data from RNNs is mixed up with non-sequence data. The output of an LSTM is representing the Sequence in an intermidiate space. That means the output of the LSTM is also a special kind of embedding.

**What does return _ state = true mean in LSTM?**

LSTM return_state=True value: When return_state parameter is True, it will output the last hidden state twice and the last cell state as the output from LSTM layer. The ouput is a three 2D-arrays of real numbers. The first dimension is indicating the number of samples (batch size) given to the LSTM layer

**Which is the first dimension of the LSTM?**

The first dimension is indicating the number of samples in the batch given to the LSTM layer The second dimension is the dimensionality of the output space defined by the units parameter in Keras LSTM implementation.

## How many populations can be separated by a LSTM?

In fact, the LSTM-based model seems to separate 3 to 4 different populations : One for students who shouldn’t be able to answer correctly (probability<10%) One for those who have a moderate to good chance to succeed (50<80%). One for those who have a very important chance of succeeding (probability>90%).

**How does a multi state LSTM work in Excel?**

Left : Basic LSTM cell / Right : Multi-state LSTM cell. Note : If you are not familiar with LSTMs you can refer to this great post by Christopher Olah. Every time a student’s label is fed to the Multi-state LSTM cell, it starts by looking up the corresponding state and previous score. Then, the student’s state is sent to update a shared LSTM cell.

**How many populations can a LSTM model separate?**

In fact, the LSTM-based model seems to separate 3 to 4 different populations : One for students who shouldn’t be able to answer correctly (probability<10%) One for those who have a moderate to good chance to succeed (50<80%).

### Is the LSTM based network similar to the MSE?

In fact, judging by the MSE alone, the two models have similar performance : LSTM-based network : 0.16 But as we discussed in a previous post, when real randomness is associated with the targets (here, predicting human behavior) we get a better assessment of the performance of a classifier by looking at its reliability measure (REL).