Fix typo in transfer-learning.md

transfer-learning.md

@@ -29,12 +29,12 @@ In practice, very few people train an entire Convolutional Network from scratch
 
 **Practical advice**. There are a few additional things to keep in mind when performing Transfer Learning:
 
-- *Constraints from pretrained models*. Note that if you wish to use a pretrained network, you may be slightly constrained in terms of the architecture you can use for your new dataset. For example, you can't arbitrarily take out Conv layers from the pretrained network. However, some changes are straight-forward: Due to parameter sharing, you can easily run a pretrained network on images of different spatial size. This is clearly evident in the case of Conv/Pool layers because their forward function is independent of the input volume spatial size (as long as the strides "fit"). In case of FC layers, this still holds true because FC layers can be converted to a Convolutional Layer: For example, in an AlexNet, the final pooling volume before the first FC layer is of size [6x6x512]. Therefore, the FC layer looking at this volume is equivalent to having a Convultional Layer that has receptive field size 6x6, and is applied with padding of 0.
+- *Constraints from pretrained models*. Note that if you wish to use a pretrained network, you may be slightly constrained in terms of the architecture you can use for your new dataset. For example, you can't arbitrarily take out Conv layers from the pretrained network. However, some changes are straight-forward: Due to parameter sharing, you can easily run a pretrained network on images of different spatial size. This is clearly evident in the case of Conv/Pool layers because their forward function is independent of the input volume spatial size (as long as the strides "fit"). In case of FC layers, this still holds true because FC layers can be converted to a Convolutional Layer: For example, in an AlexNet, the final pooling volume before the first FC layer is of size [6x6x512]. Therefore, the FC layer looking at this volume is equivalent to having a Convolutional Layer that has receptive field size 6x6, and is applied with padding of 0.
 - *Learning rates*. It's common to use a smaller learning rate for ConvNet weights that are being fine-tuned, in comparison to the (randomly-initialized) weights for the new linear classifier that computes the class scores of your new dataset. This is because we expect that the ConvNet weights are relatively good, so we don't wish to distort them too quickly and too much (especially while the new Linear Classifier above them is being trained from random initialization).
 
 <a name='tf'></a>
 ## Additional References
 
 - [CNN Features off-the-shelf: an Astounding Baseline for Recognition](http://arxiv.org/abs/1403.6382) trains SVMs on features from ImageNet-pretrained ConvNet and reports several state of the art results.
 - [DeCAF](http://arxiv.org/abs/1310.1531) reported similar findings in 2013. The framework in this paper (DeCAF) was a Python-based precursor to the C++ Caffe library.
-- [How transferable are features in deep neural networks?](http://arxiv.org/abs/1411.1792) studies the transfer learning performance in detail, including some unintuitive findings about layer co-adaptations.
+- [How transferable are features in deep neural networks?](http://arxiv.org/abs/1411.1792) studies the transfer learning performance in detail, including some unintuitive findings about layer co-adaptations.