Deep Learning - Convolutional Neural Networks with TensorFlow - Text Classification with CNNs

The video tutorial guides viewers through implementing a Convolutional Neural Network (CNN) for text classification using a prepared Colab notebook. It covers data preprocessing, model creation, and training, highlighting the differences between CNN and RNN scripts. The tutorial emphasizes the importance of understanding misclassified samples and encourages viewers to recreate the process independently.

What is the main difference between the CNN script discussed in the lecture and the previous RNN script?

• The CNN script is for image classification.

• The CNN script uses 1D operations instead of 2D.

• The CNN script uses 2D operations instead of 1D.

• The CNN script does not use any embeddings.

Which library is used to read and manipulate the data in the preprocessing step?

• Pandas

• TensorFlow

• NumPy

• Matplotlib

What is the purpose of converting text data into sequences of integers?

• To make the data compatible with neural network input requirements

• To visualize the data better

• To reduce the size of the dataset

• To improve the accuracy of the model

What is the significance of padding sequences in text data processing?

• To ensure all sequences have the same length

• To increase the vocabulary size

• To convert text to lowercase

• To remove stop words

In the CNN model architecture, what follows each convolution layer?

• Dropout layer

• Dense layer

• Max pooling layer

• Activation layer

What activation function is used in the final layer of the CNN model for binary classification?

• Tanh

• Softmax

• Sigmoid

• ReLU

What is a suggested exercise to better understand the model's performance on the dataset?

• Print out the correctly classified examples

• Print out the misclassified examples

• Increase the number of epochs

• Change the optimizer