Chatbots for Beginners: A Complete Guide to Build Chatbots - Deep Learning-Based Chatbot Architecture and Development: A

The video tutorial explains the process of using encoders for input sequences and questions. It covers the steps to perform matching using dot products and activation functions like softmax. The tutorial then describes generating responses using add and permute functions, followed by forming answers through concatenation and applying LSTM layers. Finally, it discusses using dropout and softmax for the output layer, with a brief mention of model building in the next video.

What is the output dimension of the first encoder in the input sequence?

• 256

• 64

• 128

• 32

Which activation function is applied to the match in the matching process?

• Sigmoid

• ReLU

• Tanh

• Softmax

What function is used to generate the response in the response generation process?

• Add

• Multiply

• Divide

• Subtract

In the response generation, what operation is performed after adding match and input?

• Flatten

• Reshape

• Permute

• Transpose

What is the purpose of the LSTM layer in the final steps?

• To reduce dimensionality

• To handle sequential data

• To increase model complexity

• To perform classification

What is the dropout rate used in the final steps?

• 0.1

• 0.7

• 0.3

• 0.5

Which layer is used at the end of the process to get the output sequence?

• Recurrent

• Convolutional

• Dense

• Pooling