Data Science and Machine Learning (Theory and Projects) A to Z - Sentiment Classification using RNN: RNN Setup 2

The video tutorial covers the implementation of a recurrent neural network (RNN) for sentiment classification. It begins with setting up the RNN, focusing on using sigmoid instead of softmax for binary output. The tutorial then explains how to compute the cross-entropy loss for binary outputs and modify the training function to use batch gradient descent. It addresses common errors and debugging techniques, followed by testing the RNN model on Yelp reviews. The tutorial concludes with insights into the RNN's performance and potential improvements.

What activation function is used in the RNN for sentiment classification?

• Tanh

• Sigmoid

• ReLU

• Softmax

In the modified RNN architecture, what is the significance of the last output?

• It is the only output used for prediction.

• It is discarded as it is not needed.

• It is used to initialize the next sequence.

• It is used to calculate the average output.

Which loss function is used for binary classification in this RNN?

• Huber Loss

• Cross-Entropy Loss

• Hinge Loss

• Mean Squared Error

What type of gradient descent is applied in the training function?

• Stochastic Gradient Descent

• Mini-Batch Gradient Descent

• Batch Gradient Descent

• Momentum Gradient Descent

What is a common issue when copying and pasting code in RNN implementation?

• Syntax errors

• Incorrect loop bounds

• Logical errors

• Missing variable declarations

What is the primary purpose of testing the RNN model on Yelp reviews?

• To demonstrate the classification process

• To validate the model's accuracy

• To check for overfitting

• To ensure the model can handle large datasets

What is the expected output of the RNN when the input is a positive review?

• A negative value

• A value close to 0

• A value close to 1

• A random value

What is a potential error when testing the RNN model?

• Index out of bounds

• Division by zero

• Incorrect data type

• Null pointer exception

How can the learning rate affect the RNN training process?

• It determines the number of epochs.

• It affects the speed of convergence.

• It alters the input data.

• It changes the model architecture.

What is the role of automatic differentiation in RNNs?

• It enhances the model's interpretability.

• It reduces the training time.

• It increases the model's accuracy.

• It simplifies the computation of derivatives.