Update to Rubix ML 0.3.0

LICENSE.md → LICENSE

@@ -1,6 +1,6 @@
 MIT License
 
-Copyright (c) 2020 The Rubix ML Community
+Copyright (c) 2020 Rubix ML
 Copyright (c) 2020 Andrew DalPino
 
 Permission is hereby granted, free of charge, to any person obtaining a copy

README.md

@@ -114,9 +114,9 @@ $scores = $estimator->scores();
 
 Then, we can plot the values using our favorite plotting software such as [Tableu](https://public.tableau.com/en-us/s/) or [Excel](https://products.office.com/en-us/excel-a). If all goes well, the value of the loss should go down as the value of the validation score goes up. Due to snapshotting, the epoch at which the validation score is highest and the loss is lowest is the point at which the values of the network parameters are taken.
 
-![Cross Entropy Loss](https://raw.githubusercontent.com/RubixML/CIFAR-10/master/docs/images/training-loss.svg?sanitize=true)
+![Cross Entropy Loss](https://raw.githubusercontent.com/RubixML/CIFAR-10/master/docs/images/training-losses.png)
 
-![F1 Score](https://raw.githubusercontent.com/RubixML/CIFAR-10/master/docs/images/validation-score.svg?sanitize=true)
+![F1 Score](https://raw.githubusercontent.com/RubixML/CIFAR-10/master/docs/images/validation-scores.png)
 
 ### Saving
 Before exiting the script, save the model so we can run cross validation on it in another process.
@@ -242,4 +242,4 @@ Email: akrizhevsky '@' gmail.com
 >- [1] A. Krizhevsky. (2009). Learning Multiple Layers of Features from Tiny Images.
 
 ## License
-The code is licensed [MIT](LICENSE.md) and the tutorial is licensed [CC BY-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/).
+The code is licensed [MIT](LICENSE) and the tutorial is licensed [CC BY-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/).

composer.json

@@ -3,7 +3,7 @@
     "type": "project",
     "description": "Use the famous CIFAR-10 dataset to train a multi-layer neural network to recognize images of cats, dogs, and other things.",
     "homepage": "https://github.com/RubixML/CIFAR10",
-    "license": "Apache-2.0",
+    "license": "MIT",
     "keywords": [
         "batch norm", "cifar 10", "cifar-10", "classification", "cross validation", "dataset",
         "data science", "dropout", "elu", "example project", "exponential linear unit",
@@ -14,18 +14,14 @@
     "authors": [
         {
             "name": "Andrew DalPino",
-            "email": "me@andrewdalpino.com",
-            "homepage": "https://andrewdalpino.com",
+            "homepage": "https://github.com/andrewdalpino",
             "role": "Lead Engineer"
         }
     ],
     "require": {
         "php": ">=7.2",
         "ext-gd": "*",
-        "rubix/ml": "^0.1.0"
-    },
-    "suggest": {
-        "ext-tensor": "For faster training and inference"
+        "rubix/ml": "^0.3.0"
     },
     "scripts": {
         "train": "@php train.php",