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    "# Computational Performance\n",
    ":label:`chap_performance`\n",
    "\n",
    "In deep learning, \n",
    "datasets and models are usually large,\n",
    "which involves heavy computation.\n",
    "Therefore, computational performance matters a lot.\n",
    "This chapter will focus on the major factors that affect computational performance:\n",
    "imperative programming, symbolic programming, asynchronous computing, automatic parallelism, and multi-GPU computation.\n",
    "By studying this chapter, you may further improve computational performance of those models implemented in the previous chapters,\n",
    "for example, by reducing training time without affecting accuracy.\n",
    "\n",
    ":begin_tab:toc\n",
    " - [hybridize](hybridize.ipynb)\n",
    " - [async-computation](async-computation.ipynb)\n",
    " - [auto-parallelism](auto-parallelism.ipynb)\n",
    " - [hardware](hardware.ipynb)\n",
    " - [multiple-gpus](multiple-gpus.ipynb)\n",
    " - [multiple-gpus-concise](multiple-gpus-concise.ipynb)\n",
    " - [parameterserver](parameterserver.ipynb)\n",
    ":end_tab:\n"
   ]
  }
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