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Which layer components are essential in neural networks for language models?
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Feed-forward networks and residual connections.
Why is scaled dot product attention important in the attention mechanism?
It prevents exploding gradients by scaling scores.
What differentiates an encoder from a decoder in model architecture?
The encoder processes input sequences, while the decoder generates outputs.
How does GPT architecture utilize decoder layers?
GPT uses solely decoder layers with masked attention to prevent lookahead.
What practices are recommended for optimizing model performance?
Efficiency testing, fine-tuning, and quantization.
What is the function of multi-head attention in model architecture?
It allows parallel processing of input data.
What are the main focus areas when building a large language model from scratch?
Data handling, mathematics, and the transformers behind LLMs.
What function does the 'nn.Module' serve in model initialization?
It is used for parameter tracking.
How are embeddings utilized in building a large language model?
Embeddings represent discrete inputs as dense vectors.
Which loss function is typically used for evaluating model predictions?
Cross-entropy.
What is the purpose of using tokenizers in a language model?
Tokenizers are used for converting characters to integers.
Why is standard deviation important when initializing weights in a neural network?
Proper standard deviation is crucial for effective training.
What optimizer is recommended in this course for training the language model?
AdamW optimizer for weight decay and momentum.
What tools are required for setting up the environment to build a large language model?
Jupyter notebooks, Anaconda prompt, CUDA, and a virtual environment.
What role does dropout play in training a language model?
Dropout is used to prevent overfitting.
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