Merge pull request #997 from ozancaglayn/dev

  [docs] Several documentation fixes

docs/installation.md

@@ -96,9 +96,9 @@ On **CentOS / RHEL / Fedora**, most of the dependencies can be installed with
 
 The Google flags library, Google logging library and LMDB already made their ways into newer versions of **CentOS / RHEL / Fedora** so it is better to first attempt to install them using `yum`
 
-    sudo yum install gflags-devel glog-devel lmdb
+    sudo yum install gflags-devel glog-devel lmdb-devel
 
-**Finally** in case you couldn't find those 3 libraries in your distribution's repositories, here are the instructions to follow for manually building and installing them on **Ubuntu 12.04 / CentOS / RHEL / Fedora**
+**Finally** in case you couldn't find those extra libraries mentioned above in your distribution's repositories, here are the instructions to follow for manually building and installing them on **Ubuntu 12.04 / CentOS / RHEL / Fedora** (or practically on any Linux distribution)
 
     # glog
     wget https://google-glog.googlecode.com/files/glog-0.3.3.tar.gz
@@ -232,7 +232,7 @@ There is an unofficial Windows port of Caffe at [niuzhiheng/caffe:windows](https
 
 ## Compilation
 
-Now that you have the prerequisites, edit your `Makefile.config` to change the paths for your setup.
+Now that you have the prerequisites, edit your `Makefile.config` to change the paths for your setup (you should especially uncomment and set `BLAS_LIB` accordingly on distributions like **CentOS / RHEL / Fedora** where ATLAS is installed under `/usr/lib[64]/atlas`)
 The defaults should work, but uncomment the relevant lines if using Anaconda Python.
 
     cp Makefile.config.example Makefile.config

examples/mnist/readme.md

@@ -26,13 +26,13 @@ If it complains that `wget` or `gunzip` are not installed, you need to install t
 
 Before we actually run the training program, let's explain what will happen. We will use the [LeNet](http://yann.lecun.com/exdb/publis/pdf/lecun-01a.pdf) network, which is known to work well on digit classification tasks. We will use a slightly different version from the original LeNet implementation, replacing the sigmoid activations with Rectified Linear Unit (ReLU) activations for the neurons.
 
-The design of LeNet contains the essence of CNNs that are still used in larger models such as the ones in ImageNet. In general, it consists of a convolutional layer followed by a pooling layer, another convolution layer followed by a pooling layer, and then two fully connected layers similar to the conventional multilayer perceptrons. We have defined the layers in `CAFFE_ROOT/data/lenet.prototxt`.
+The design of LeNet contains the essence of CNNs that are still used in larger models such as the ones in ImageNet. In general, it consists of a convolutional layer followed by a pooling layer, another convolution layer followed by a pooling layer, and then two fully connected layers similar to the conventional multilayer perceptrons. We have defined the layers in `CAFFE_ROOT/examples/lenet.prototxt`.
 
 ## Define the MNIST Network
 
 This section explains the prototxt file `lenet_train.prototxt` used in the MNIST demo. We assume that you are familiar with [Google Protobuf](https://developers.google.com/protocol-buffers/docs/overview), and assume that you have read the protobuf definitions used by Caffe, which can be found at [src/caffe/proto/caffe.proto](https://github.com/Yangqing/caffe/blob/master/src/caffe/proto/caffe.proto).
 
-Specifically, we will write a `caffe::NetParameter` (or in python, `caffe.proto.caffe_pb2.NetParameter`) protubuf. We will start by giving the network a name:
+Specifically, we will write a `caffe::NetParameter` (or in python, `caffe.proto.caffe_pb2.NetParameter`) protobuf. We will start by giving the network a name:
 
     name: "LeNet"