Enrollments closing soon for Post Graduate Certificate Program in AI & Machine Learning By E&ICT IIT Roorkee | 3 Seats Left
Login using Social Account
Login using your credentials
RNN cannot work on sequences of arbitrary lengths of input
True
False
Taking you to the next exercise in seconds...
Stay here Next Exercise
Want to create exercises like this yourself? Click here.
Note - Having trouble with the assessment engine? Follow the steps listed here
No hints are availble for this assesment
1 Recurrent Neural Networks - Introduction to RNN
2 MCQ - RNN cannot work on sequences of arbitrary lengths of input
3 MCQ - RNN can analyze time series data such as stock prices
4 MCQ - RNN looks much like a feedforward neural network except it has connections pointing backward
5 MCQ - A part of a neural network that preserves some state across time steps is called a memory cell
6 Recurrent Neural Networks - Forecasting a Time Series
7 MCQ - In an encoder-decoder network, we have sequence-to-vector network, called an decoder followed by vector-to-sequence network, called a encoder
8 MCQ - To train an RNN, the trick is to unroll it through time and then simply use regular backpropagation. This strategy is called backpropagation through time (BPTT).
9 MCQ - When the data is a sequence of one or more values per time step it is called a time series
10 MCQ - Univariate time series involves two or more input variables
11 MCQ - The task to predict missing values from the past is called imputation
12 Recurrent Neural Networks - Forecasting several Time Steps ahead
13 MCQ - What is/are the option(s) to predict 10 time steps ahead using an RNN?
14 Recurrent Neural Networks - Handling Long Sequences
15 MCQ - The Long Short-Term Memory (LSTM) cell was proposed by Sepp Hochreiter and Jürgen Schmidhuber in the year
16 MCQ - The Gated Recurrent Unit (GRU) cell was proposed by Kyunghyun Cho et al. in the year
17 MCQ - GRU can be implemented in Keras using the keras.layers.GRU layer
Loading comments...