Deep Learning with Python (Video 17)

The video tutorial covers sentiment analysis using a recurrent neural network with Keras. It begins with an introduction to sentiment analysis, highlighting the importance of understanding context. The IMDB movie review dataset is used for training, with preprocessing steps explained. The tutorial then delves into setting up word embeddings and the model architecture, including LSTM layers. Training and evaluation processes are discussed, achieving an accuracy of 82%. The video concludes with a comparison of recurrent and convolutional layers and hints at future challenges in deep learning.

What is the main challenge in determining the sentiment of a text?

• Translating the text into another language

• Counting the number of positive words

• Understanding the context of the entire phrase

• Identifying the subject of the text

Which dataset is used for training the sentiment analysis model?

• IMDB Movie Reviews

• Yelp Reviews

• Twitter Sentiment Dataset

• Amazon Product Reviews

What is the purpose of padding sequences in text preprocessing?

• To increase the dataset size

• To ensure all sequences are of the same length

• To remove noise from the data

• To enhance the model's accuracy

What is the role of the embedding layer in a neural network?

• To increase the training speed

• To reduce the dimensionality of the data

• To map word IDs to vectors

• To classify the input data

Which of the following is a popular word vector representation model?

• VGGNet

• AlexNet

• Word2Vec

• ResNet

What is the output of the LSTM layer in the RNN model?

• A sequence of 256-dimensional vectors

• A sequence of word IDs

• A single 128-dimensional vector

• A single 120-dimensional vector

What accuracy did the RNN model achieve on the test set?

• 75%

• 95%

• 82%

• 90%