Fix docstring typos

Author: christiangnrd

src/convnets/resnets/resnext.jl

@@ -7,11 +7,11 @@ Creates a ResNeXt model with the specified depth, cardinality, and base width.
 
 # Arguments
 
-  - `depth`: one of `[50, 101, 152]`. The depth of the ResNet model.
+  - `depth`: one of `[50, 101, 152]`. The depth of the ResNeXt model.
 
   - `pretrain`: set to `true` to load the model with pre-trained weights for ImageNet.
     Supported configurations are:
-    
+
       + depth 50, cardinality of 32 and base width of 4.
       + depth 101, cardinality of 32 and base width of 8.
       + depth 101, cardinality of 64 and base width of 4.

src/convnets/resnets/seresnet.jl

@@ -6,13 +6,13 @@ Creates a SEResNet model with the specified depth.
 
 # Arguments
 
-  - `depth`: one of `[18, 34, 50, 101, 152]`. The depth of the ResNet model.
+  - `depth`: one of `[18, 34, 50, 101, 152]`. The depth of the SEResNet model.
   - `pretrain`: set to `true` to load the model with pre-trained weights for ImageNet
   - `inchannels`: the number of input channels.
   - `nclasses`: the number of output classes
 
 !!! warning
-    
+
     `SEResNet` does not currently support pretrained weights.
 
 Advanced users who want more configuration options will be better served by using [`resnet`](@ref).
@@ -49,15 +49,15 @@ Creates a SEResNeXt model with the specified depth, cardinality, and base width.
 
 # Arguments
 
-  - `depth`: one of `[50, 101, 152]`. The depth of the ResNet model.
+  - `depth`: one of `[50, 101, 152]`. The depth of the SEResNeXt model.
   - `pretrain`: set to `true` to load the model with pre-trained weights for ImageNet
   - `cardinality`: the number of groups to be used in the 3x3 convolution in each block.
   - `base_width`: the number of feature maps in each group.
   - `inchannels`: the number of input channels
   - `nclasses`: the number of output classes
 
 !!! warning
-    
+
     `SEResNeXt` does not currently support pretrained weights.
 
 Advanced users who want more configuration options will be better served by using [`resnet`](@ref).