Fixed figures

Author: ashfarhangi

.obsidian/workspace

@@ -4,12 +4,12 @@
     "type": "split",
     "children": [
       {
-        "id": "0e600774d70c3e3b",
+        "id": "0fa210146851d0e9",
         "type": "leaf",
         "state": {
           "type": "markdown",
           "state": {
-            "file": "medical.md",
+            "file": "README.md",
             "mode": "source",
             "source": false
           }
@@ -55,12 +55,11 @@
             "state": {
               "type": "outline",
               "state": {
-                "file": "medical.md"
+                "file": "README.md"
               }
             }
           }
-        ],
-        "currentTab": 2
+        ]
       }
     ],
     "direction": "horizontal",
@@ -80,7 +79,7 @@
             "state": {
               "type": "backlink",
               "state": {
-                "file": "medical.md",
+                "file": "README.md",
                 "collapseAll": false,
                 "extraContext": false,
                 "sortOrder": "alphabetical",
@@ -97,13 +96,17 @@
     "direction": "horizontal",
     "width": 300
   },
-  "active": "0e600774d70c3e3b",
+  "active": "0fa210146851d0e9",
   "lastOpenFiles": [
-    "medical.md",
     "README.md",
-    "media/1f11fb9b2ed3eb432764db5635531f54.png",
-    "Archive/README - Copy.md",
-    "~$README.md",
-    "Archive/README - Copy (2).md"
+    "Pasted.md",
+    "Pasted_image_20220125184741.png.md",
+    "media/array.png.md",
+    "media/sol.png",
+    "media/runfi.png",
+    "media/linked.png",
+    "media/runtime.png",
+    "media/best.png",
+    "media/types.png"
   ]
 }

Pasted image 20220125184838.png

@@ -26,7 +26,7 @@ My personal experience stems from PhD courses at UCF and Udacity nanodegree:
 ## 1. Orientation to the Machine Learning Foundations Series
 - [Data Structures, Algorithms, and Machine Learning Optimization](https://www.oreilly.com/library/view/data-structures-algorithms/9780137644889/)
 - The foundations should be strong. By large, CS concepts are standalone (no need for linear algebra, ...)
-![](Pasted image 20220123111220.png)
+![](media/outline.png)
 - Reasons for using DSA in ML:
 	- Finding the correct DS for various situations  
 	- Be thoughtful for time/space complexity in:
@@ -38,26 +38,26 @@ My personal experience stems from PhD courses at UCF and Udacity nanodegree:
 
 
 ## 2. Big O Notion
-![](Pasted image 20220124174718.png)
+![](media/runfi.png)
 
 
-![](Pasted image 20220124175807.png)
+![](media/runtime.png)
 - Shipping a dataset to datacenter has constant time (e.g. 24 hours). But uploading it to server is not.
 - For problems its advisable to find the best, worst and expected (average) Big O
 - 
-![](Pasted image 20220124180150.png)
+![](media/arrays.png)
 
 We keep the *m*. Given that in might be large.
-![](Pasted image 20220124180632.png)
+![](media/sol.png)
 # 3. Data structures
 List
-![](Pasted image 20220125184741.png)
-![](Pasted image 20220125184854.png)
+![](types.png)
+![](media/best.png)
 
 Linked lists are not indexed. Only nodes are linked toghether.
-![](Pasted image 20220125185234.png)
+![](media/linked.png)
 
-![](Pasted image 20220125185335.png)
+![](media/double.png)
 - Stacks are implemented as lists in python. s.append, s.pop
 - Queue: beginning and end are available. You can also take a peek of the first one.
 - Deques (دک) (Double ended- queue):