Compare float types and design ablation

Q: Compare float types and design ablation

This question evaluates understanding of floating-point numerical representations and experimental design for ablation studies, testing competencies in numerical precision, dynamic range, memory and performance trade-offs, detection of underflow/overflow/rounding errors, and the ability to design controlled experiments.

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Floating-point types and ablation study design

You are training deep neural networks on modern accelerators that support multiple floating-point formats (for example, float64, float32, float16, and bfloat16).

Answer the following:

What are the main differences between common floating-point types used in deep learning (e.g., float64, float32, float16, bfloat16)? Describe their trade-offs in terms of numerical precision, dynamic range, memory usage, and training speed.
During training, how can you detect that numerical precision loss (underflow, overflow, or excessive rounding error) is causing problems for your model?
Suppose you want to evaluate the impact of using different floating-point precisions on model quality and training stability. Design an ablation study to isolate and measure the effect of precision choice.

Compare float types and design ablation

Floating-point types and ablation study design

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Compare float types and design ablation

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Floating-point types and ablation study design

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