Add docs; fixes #4

prefix/algorithms/amohammadi/facenet_projection_and_comparison/1.json

 {
     "api_version": 2,
+    "description": "A face recognition algorithm to compare one probe image against a set of template images.",
     "groups": [
         {
             "inputs": {

prefix/algorithms/amohammadi/facenet_projection_and_comparison/1.rst

+A face recognition algorithm to compare one probe image against a set of template images.
+The images must be gray-scale and should contain the face region only.
+Internally, the images are resized to 160x160 pixels.
+This algorithm expects the pre-trained FaceNet model to be provided as input as well.
+The model can be downloaded from https://drive.google.com/file/d/0B5MzpY9kBtDVZ2RpVDYwWmxoSUk
+which was made available in https://github.com/davidsandberg/facenet/tree/b95c9c3290455cabc425dc3f9435650679a74c50
+

prefix/algorithms/amohammadi/linear_machine_projection_and_comparison/1.json

 {
     "api_version": 2,
+    "description": "A biometrics algorithm that compares a probe image to a set of template images.",
     "groups": [
         {
             "inputs": {

prefix/algorithms/amohammadi/linear_machine_projection_and_comparison/1.rst

+A biometrics algorithm that compares a probe image to a set of template images
+and outputs a comparison score.
+This algorithm was trained on the ATNT database and reproduces the EigenFaces
+face recognition baseline.
+The input images must be gray-scale and of the size of 92x92.

prefix/algorithms/amohammadi/mnist_tester/1.json

 {
     "api_version": 2,
-    "description": "Tests a multinomial logistic regression model",
+    "description": "Tests a logistic regression model on MNIST images",
     "groups": [
         {
             "inputs": {
@@ -28,4 +28,4 @@
     "schema_version": 2,
     "splittable": false,
     "type": "sequential"
-}
+}
\ No newline at end of file

prefix/algorithms/amohammadi/mnist_tester/1.rst

+This algorithm contains a logistic regression model trained on the MNIST database.
+It takes as input images of digits and outputs the classification label of images.