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Fix typos regarding SGD, and make links specific #2

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Jan 12, 2017
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Fix typos regarding SGD, and make links specific #2

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10 changes: 5 additions & 5 deletions Deep Learning with PyTorch.ipynb
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Expand Up @@ -720,14 +720,14 @@
"- updating the weights of the network\n",
"\n",
"The simplest update rule used in practice is the Stochastic Gradient Descent (SGD):\n",
"> `weight = weight = learning_rate * gradient`\n",
"> `weight = weight - learning_rate * gradient`\n",
"\n",
"We can implement this using simple python code:\n",
"\n",
"```python\n",
"learning_rate = 0.01\n",
"for f in net.parameters():\n",
" f.data.add_(f.grad * learning_rate)\n",
" f.data.sub_(f.grad * learning_rate)\n",
"```\n",
"\n",
"However, as you use neural networks, you want to use various different update rules such as SGD, Nesterov-SGD, Adam, RMSProp, etc.\n",
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"source": [
"## Where do I go next?\n",
"\n",
"- [Train neural nets to play video games](https://github.com/pytorch/tutorials)\n",
"- [Train a state-of-the-art ResNet network on imagenet](https://github.com/pytorch/examples)\n",
"- [Train an face generator using Generative Adversarial Networks](https://github.com/pytorch/examples)\n",
"- [Train neural nets to play video games](https://github.com/pytorch/tutorials/blob/master/Reinforcement%20(Q-)Learning%20with%20PyTorch.ipynb)\n",
"- [Train a state-of-the-art ResNet network on imagenet](https://github.com/pytorch/examples/tree/master/imagenet)\n",
"- [Train an face generator using Generative Adversarial Networks](https://github.com/pytorch/examples/tree/master/dcgan)\n",
"- [Train a word-level language model using Recurrent LSTM networks](https://github.com/pytorch/examples/tree/master/word_language_model)\n",
"- [More examples](https://github.com/pytorch/examples)\n",
"- [More tutorials](https://github.com/pytorch/tutorials)\n",
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