Advanced Chatbots with Deep Learning and Python - Model Completion

The video tutorial guides viewers through the process of setting up, compiling, and training a machine learning model. It covers initializing the model with input sequences, compiling it with an optimizer and loss function, and providing a summary of the model's architecture. The tutorial then demonstrates how to fit the model using training data, specifying batch size and epochs, and evaluating its accuracy. Finally, it discusses visualizing the results and saving the model for future use.

What is the initial step in setting up the model?

• Defining the optimizer

• Visualizing the data

• Running the model

• Importing necessary packages

Which optimizer is used in the model compilation?

• Adam

• SGD

• RMSProp

• Adagrad

What is the purpose of the model summary?

• To compile the model

• To fit the model

• To list the layers used in the model

• To visualize the data

What is the batch size used during model fitting?

• 128

• 64

• 32

• 16

How many epochs are used for training the model?

• 15

• 10

• 20

• 25

What tool is used to visualize the training and testing accuracies?

• Seaborn

• Pandas

• Matplotlib

• Scikit-learn

What does it indicate if the test accuracy is close to the train accuracy?

• The model needs more data

• The model has learned well

• The model is underfitting

• The model is overfitting