Types of Learning

Projects/TensorFlow/Schemas/AppSchema.json

@@ -76,6 +76,30 @@
                 "label": "Add Execution Environment",
                 "relatedUiObject": "Execution Environment",
                 "actionFunction": "payload.executeAction"
+            },
+            {
+                "action": "Add UI Object",
+                "disableIfPropertyIsDefined": true,
+                "propertyToCheckFor": "typeOfLearning",
+                "label": "Add Supervised Learning",
+                "relatedUiObject": "Supervised Learning",
+                "actionFunction": "payload.executeAction"
+            },
+            {
+                "action": "Add UI Object",
+                "disableIfPropertyIsDefined": true,
+                "propertyToCheckFor": "typeOfLearning",
+                "label": "Add Unsupervised Learning",
+                "relatedUiObject": "Unsupervised Learning",
+                "actionFunction": "payload.executeAction"
+            },
+            {
+                "action": "Add UI Object",
+                "disableIfPropertyIsDefined": true,
+                "propertyToCheckFor": "typeOfLearning",
+                "label": "Add Reinforcement Learning",
+                "relatedUiObject": "Reinforcement Learning",
+                "actionFunction": "payload.executeAction"
             }
         ],
         "addLeftIcons": true,
@@ -89,10 +113,94 @@
                 "name": "executionEnvironment",
                 "type": "node",
                 "childType": "Execution Environment"
+            },
+            {
+                "name": "typeOfLearning",
+                "type": "node",
+                "childType": "Supervised Learning"
+            },
+            {
+                "name": "typeOfLearning",
+                "type": "node",
+                "childType": "Unsupervised Learning"
+            },
+            {
+                "name": "typeOfLearning",
+                "type": "node",
+                "childType": "Reinforcement Learning"
             }
         ],
         "icon": "tensorflow"
     },
+    {
+        "type": "Supervised Learning",
+        "menuItems": [
+            {
+                "action": "Delete UI Object",
+                "askConfirmation": true,
+                "confirmationLabel": "Confirm to Delete",
+                "label": "Delete",
+                "iconPathOn": "delete-entity",
+                "iconPathOff": "delete-entity",
+                "actionFunction": "payload.executeAction"
+            }
+        ],
+        "addLeftIcons": true,
+        "level": 3,
+        "attachingRules": {
+            "compatibleTypes": "->TensorFlow Library->"
+        },
+        "propertyNameAtParent": "typeOfLearning",
+        "childrenNodesProperties": [
+        ],
+        "icon": "tensorflow"
+    },
+    {
+        "type": "Unsupervised Learning",
+        "menuItems": [
+            {
+                "action": "Delete UI Object",
+                "askConfirmation": true,
+                "confirmationLabel": "Confirm to Delete",
+                "label": "Delete",
+                "iconPathOn": "delete-entity",
+                "iconPathOff": "delete-entity",
+                "actionFunction": "payload.executeAction"
+            }
+        ],
+        "addLeftIcons": true,
+        "level": 3,
+        "attachingRules": {
+            "compatibleTypes": "->TensorFlow Library->"
+        },
+        "propertyNameAtParent": "typeOfLearning",
+        "childrenNodesProperties": [
+        ],
+        "icon": "tensorflow"
+    },
+    {
+        "type": "Reinforcement Learning",
+        "menuItems": [
+            {
+                "action": "Delete UI Object",
+                "askConfirmation": true,
+                "confirmationLabel": "Confirm to Delete",
+                "label": "Delete",
+                "iconPathOn": "delete-entity",
+                "iconPathOff": "delete-entity",
+                "actionFunction": "payload.executeAction"
+            }
+        ],
+        "addLeftIcons": true,
+        "level": 3,
+        "attachingRules": {
+            "compatibleTypes": "->TensorFlow Library->"
+        },
+        "propertyNameAtParent": "typeOfLearning",
+        "childrenNodesProperties": [
+        ],
+        "icon": "tensorflow"
+    },
     {
         "type": "Execution Environment",
         "menuItems": [

Projects/TensorFlow/Schemas/Docs-Nodes/reinforcement-learning.json

@@ -0,0 +1,19 @@
+{
+    "type": "Reinforcement Learning",
+    "definition": {
+        "text": "Reinforcement learning (RL) is an area of machine learning concerned with how intelligent agents ought to take actions in an environment in order to maximize the notion of cumulative reward. Reinforcement learning is one of three basic machine learning paradigms, alongside supervised learning and unsupervised learning.",
+        "updated": 1613197154946
+    },
+    "paragraphs": [
+        {
+            "style": "Text",
+            "text": "Reinforcement learning differs from supervised learning in not needing labelled input/output pairs be presented, and in not needing sub-optimal actions to be explicitly corrected. Instead the focus is on finding a balance between exploration (of uncharted territory) and exploitation (of current knowledge).",
+            "updated": 1613197179841
+        },
+        {
+            "style": "Text",
+            "text": "The environment is typically stated in the form of a Markov decision process (MDP), because many reinforcement learning algorithms for this context use dynamic programming techniques. The main difference between the classical dynamic programming methods and reinforcement learning algorithms is that the latter do not assume knowledge of an exact mathematical model of the MDP and they target large MDPs where exact methods become infeasible.",
+            "updated": 1613197190452
+        }
+    ]
+}

Projects/TensorFlow/Schemas/Docs-Nodes/supervised-learning.json

@@ -0,0 +1,14 @@
+{
+    "type": "Supervised Learning",
+    "definition": {
+        "text": "Supervised learning is the machine learning task of learning a function that maps an input to an output based on example input-output pairs. It infers a function from labeled training data consisting of a set of training examples. In supervised learning, each example is a pair consisting of an input object (typically a vector) and a desired output value (also called the supervisory signal).",
+        "updated": 1613197014944
+    },
+    "paragraphs": [
+        {
+            "style": "Text",
+            "text": "A supervised learning algorithm analyzes the training data and produces an inferred function, which can be used for mapping new examples. An optimal scenario will allow for the algorithm to correctly determine the class labels for unseen instances. This requires the learning algorithm to generalize from the training data to unseen situations in a \"reasonable\" way (see inductive bias).",
+            "updated": 1613197051912
+        }
+    ]
+}

Projects/TensorFlow/Schemas/Docs-Nodes/unsupervised-learning.json

@@ -0,0 +1,14 @@
+{
+    "type": "Unsupervised Learning",
+    "definition": {
+        "text": "Unsupervised learning (UL) is a type of algorithm that learns patterns from untagged data. The hope is that through mimicry, the machine is forced to build a compact internal representation of its world. ",
+        "updated": 1613197508073
+    },
+    "paragraphs": [
+        {
+            "style": "Text",
+            "text": "In contrast to Supervised Learning (SL) where data is tagged by a human, eg. as \"car\" or \"fish\" etc, UL exhibits self-organization that captures patterns as neuronal predelections or probability densities. The other levels in the supervision spectrum are Reinforcement Learning where the machine is given only a numerical performance score as its guidance, and Semi-supervised learning where a smaller portion of the data is tagged. Two broad methods in UL are Neural Networks and Probabilistic Methods.",
+            "updated": 1613197533736
+        }
+    ]
+}