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docs: refresh build guidance and fix typos #101

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8 changes: 4 additions & 4 deletions docs-site/docs/autograd.md
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Expand Up @@ -8,8 +8,8 @@ title: Autograd
There are two typical ways to implement autograd, via symbolic differentiation
like [Theano](http://deeplearning.net/software/theano/index.html) or reverse
differentiation like
[Pytorch](https://pytorch.org/docs/stable/notes/autograd.html). SINGA follows
Pytorch way, which records the computation graph and apply the backward
[PyTorch](https://pytorch.org/docs/stable/notes/autograd.html). SINGA follows
PyTorch way, which records the computation graph and apply the backward
propagation automatically after forward propagation. The autograd algorithm is
explained in details
[here](https://pytorch.org/docs/stable/notes/autograd.html). We explain the
Expand Down Expand Up @@ -58,7 +58,7 @@ There are two member functions for forwarding and backwarding, i.e.,
`CTensor`), and output `Ctensor`s. To add a specific operation, subclass
`operation` should implement their own `.forward()` and `.backward()`. The
`backward()` function is called by the `backward()` function of autograd
automatically during backward propogation to compute the gradients of inputs
automatically during backward propagation to compute the gradients of inputs
(according to the `require_grad` field).

### Layer
Expand Down Expand Up @@ -128,7 +128,7 @@ for i in range(10):

### Operation + Layer

The following [example](https://github.com/apache/singa/blob/master/examples/autograd/mnist_cnn.py) implemeNts a CNN model using layers provided by the autograd module.
The following [example](https://github.com/apache/singa/blob/master/examples/autograd/mnist_cnn.py) implements a CNN model using layers provided by the autograd module.

#### Create the layers

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2 changes: 1 addition & 1 deletion docs-site/docs/benchmark-train.md
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Expand Up @@ -11,7 +11,7 @@ as the application. ResNet-50 has 50 convolution layers for image
classification. It requires 3.8 GFLOPs to pass a single image (of size 224x224)
through the network. The input image size is 224x224.

Hardware: we use p2.8xlarge instances from AWS, each of which has 8 Nvidia Tesla
Hardware: we use p2.8xlarge instances from AWS, each of which has 8 NVIDIA Tesla
K80 GPUs, 96 GB GPU memory in total, 32 vCPU, 488 GB main memory, 10 Gbps
network bandwidth.

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