Computational Performance
🏷️chap_performance
In deep learning,
datasets and models are usually large,
which involves heavy computation.
Therefore, computational performance matters a lot.
This chapter will focus on the major factors that affect computational performance:
imperative programming, symbolic programming, asynchronous computing, automatic parallelism, and multi-GPU computation.
By studying this chapter, you may further improve computational performance of those models implemented in the previous chapters,
for example, by reducing training time without affecting accuracy.
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