Fundamentals of Neural Networks - Lab 1 - RNN in Text Classification

This video tutorial covers the design and implementation of recurrent neural networks (RNNs) for text classification using the IMDb dataset. It explains data processing techniques, including converting text to tokens and handling special characters. The tutorial guides viewers through building and training an RNN model using TensorFlow, highlighting the use of bidirectional RNNs and LSTM layers. It also explores advanced RNN architectures and discusses the impact of model depth on performance.

What type of neural network is particularly suited for handling sequential data like text?

• Convolutional Neural Network

• Feedforward Neural Network

• Recurrent Neural Network

• Artificial Neural Network

What dataset is used in the lab for text classification?

• IMDb

• COCO

• MNIST

• CIFAR-10

What is the purpose of converting words into tokens or numbers in text processing?

• To enable numerical computation

• To reduce data size

• To improve readability

• To increase vocabulary

What symbol is used to replace unknown words during text processing?

• UNKNOWN

• NULL

• N/A

• UNK

Which layer in the RNN model helps in handling sequential dependencies?

• LSTM Layer

• Convolutional Layer

• Dense Layer

• Dropout Layer

What does a bidirectional RNN do?

• Increases model complexity

• Processes data in both directions

• Reduces training time

• Processes data in one direction

What is the advantage of training a model on a GPU?

• Faster computation

• Lower cost

• Better accuracy

• Simpler code

What is the typical accuracy achieved by the RNN model after training?

• 70%

• 80%

• 86%

• 90%

What is a potential downside of simply adding more layers to an RNN?

• Increased accuracy

• Decreased training time

• Simpler architecture

• No guaranteed performance improvement

What is a key consideration when deciding to make an RNN architecture deeper?

• Feature importance

• Training speed

• Model size

• Data type