10 Questions
Which component of the Transformer architecture is responsible for generating probabilities for the next possible word or other targets?
Softmax
What is the main advantage of the Transformer architecture in terms of scalability?
It is highly parallelizable
In a full Transformer model, what is the role of the encoder?
Processes the input sequence
What makes the Transformer architecture highly flexible?
It has configurable architecture
What is the purpose of parameter sharing in some Transformer variants?
To reduce the total number of parameters
Which element of the Transformer architecture is responsible for addressing the vanishing gradient problem in deep neural networks?
Residual Connections
What is the purpose of positional encodings in the Transformer architecture?
To give the model information about the positions of the words in the sequence
Which normalization technique is commonly used in Transformers to stabilize and accelerate training of deep networks?
Layer Normalization
What is the core innovation in the Transformer architecture that helps the model focus on different parts of the input sequence when producing an output?
Attention Mechanism
What type of neural network is applied to each position separately and identically in each layer of the Transformer?
Feed-Forward Neural Network
Test your knowledge on the key elements of the transformer architecture, including attention mechanisms and positional encoding. Explore how these components contribute to the model's ability to process input sequences and generate accurate outputs.
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