Advanced Chatbots with Deep Learning and Python - Predictions

The video tutorial demonstrates how to use a deep learning model to predict results based on test data. It involves setting up prediction results, visualizing test data, and extracting maximum values from predictions. The process includes tokenization and validation to determine yes or no answers. The tutorial concludes by highlighting the chatbot's accuracy and performance, which is over 80%.

What is the initial step in generating predictions using a pre-trained model?

• Checking the tokenizer

• Visualizing the data

• Inputting test data and queries

• Evaluating the model's accuracy

What is the purpose of using NP dot argument maximum in the prediction process?

• To visualize the data

• To evaluate model accuracy

• To input test data

• To extract the maximum value from predictions

How does the model determine if John is in the kitchen?

• By checking the test data

• By using a random guess

• By evaluating the tokenizer

• By visualizing the predictions

What is the role of the tokenizer in evaluating predictions?

• To input test data

• To check if the predicted answer matches the expected answer

• To visualize predictions

• To generate random predictions

What is the reported accuracy range of the deep learning chatbot model?

• 60% to 70%

• 70% to 80%

• 80% to 85%

• 50% to 60%