Update

Author: mcaliman

…data-science/2023-05-15-ai-resource-1.md …intelligence/2023-05-15-ai-resource-1.md_posts/en/computer-science/data-science/2023-05-15-ai-resource-1.md renamed to _posts/en/computer-science/artificial-intelligence/2023-05-15-ai-resource-1.md _posts/en/computer-science/data-science/2023-05-15-ai-resource-1.md renamed to _posts/en/computer-science/artificial-intelligence/2023-05-15-ai-resource-1.md

@@ -3,7 +3,7 @@ layout: post
 lang: en
 title:  "Explore the World of Artificial Intelligence"
 excerpt: "Five Websites to Boost Your Productivity and Creativity"
-category: data-science
+category: artificial-intelligence
 date: 2023-05-15 05:45:33
 tags: [English,Artificial Intelligence]
 comments: true

_posts/en/computer-science/artificial-intelligence/machine-learning/2023-09-23-machine-learning.md

@@ -0,0 +1,50 @@
+---
+layout: post
+lang: en
+title:  "Introduction to Machine Learning"
+excerpt: "Machine learning is a powerful technology that allows computers to learn from data without being explicitly programmed. It is used in a wide range of applications, including image recognition, text classification, and event prediction"
+category: artificial-intelligence
+date:   2014-09-23 22:45:33
+tags: [English,"Machine Learning"]
+comments: true
+share: true
+---
+
+
+
+Machine learning is a branch of artificial intelligence that deals with the development of algorithms that can learn from data without being explicitly programmed. In other words, machine learning allows computers to learn from information without being provided with precise instructions.
+
+Machine learning is a powerful technology that can be used to solve a wide range of problems, including:
+
+* Image and video recognition
+* Text classification
+* Event prediction
+* Information retrieval
+* System control
+
+There are different types of machine learning algorithms, each with its own characteristics and strengths. The most common types of machine learning algorithms include:
+
+* Supervised learning, in which the algorithm is trained on a dataset of input and output examples.
+* Unsupervised learning, in which the algorithm is trained on a dataset of input examples without output.
+* Semi-supervised learning, in which the algorithm is trained on a dataset of input and output examples, but only a portion of the examples have output.
+
+Machine learning is based on a variety of mathematical tools, including:
+
+* Statistics
+* Probability
+* Calculus
+* Data science
+
+Machine learning is a rapidly evolving technology, with new algorithms and techniques being developed constantly. Machine learning is having a significant impact on a wide range of industries, including healthcare, finance, education, and manufacturing.
+
+Some examples of machine learning applications
+
+Here are some examples of machine learning applications:
+
+* Facial recognition, used to unlock mobile phones, access computer systems, and identify people in images.
+* Speech recognition, used to control electronic devices, translate languages, and answer questions.
+* Image classification, used to organize images into categories, such as landscapes, animals, or people.
+* Sales forecasting, used by businesses to forecast demand for products and services.
+* Drone control, used to fly drones safely and efficiently.
+
+Machine learning is a powerful technology that has the potential to improve our lives in many ways.

_posts/en/computer-science/data-structures-and-algorithms/2014-10-10-generic-visit-algorithm-for-a-tree.md

@@ -0,0 +1,34 @@
+---
+layout: post
+lang: en
+title:  "Generic visit algorithm for a tree"
+excerpt: ""
+category: data-structures-and-algorithms
+date:   2014-10-10 22:45:33
+tags: [English,"Data Structures","Algorithms"]
+comments: true
+share: true
+---
+
+One of the most common tasks to be performed on a tree is to perform a visit to it, i.e. to review each of its nodes. By the natural language term 'reviewing' we mean 'applying some function' to the node under examination, even if it is simply printing on the screen that it has been visited.
+
+The most generic possible visiting procedure can be seen in the pseudocode algorithm below
+
+```
+proc generic_tree_visit(node r)
+   S ← { r }
+   while S ≠ ∅ do
+       u ← get node from S
+       visit(u)
+       S ← S ∪ { children of u}
+   od
+end proc
+```
+
+the algorithm keeps instant by instant in `S` the nodes representing the branch points left over and from which the visit must continue, we say that these nodes are open and form a fringe of the tree, a node becomes closed when it is removed from `S`.
+
+Concerning costs in terms of space occupied in memory and time for execution we have the following theorem.
+
+Theorem: The generic visiting algorithm applied to the root of a tree with n n nodes terminates in `O(n)` iterations, and the space used is `O(n)`.
+
+This is self-evident in itself and is formally provable. Demonstrating it is beyond the scope of this post, which is only to lay the foundations for subsequent posts which explore the theoretical and practical aspects in more detail.

_posts/en/computer-science/data-structures-and-algorithms/2014-10-12-depth-first-search-tree.md

@@ -0,0 +1,54 @@
+---
+layout: post
+lang: en
+title:  "DFS o Depth First Search"
+excerpt: ""
+category: data-structures-and-algorithms
+date:   2014-10-12 22:45:33
+tags: [English,"Data Structures","Algorithms"]
+comments: true
+share: true
+---
+
+
+
+Starting from the generic algorithm shown and using a Stack/Stack to represent `S`, we obtain the depth first search (or DFS)
+
+```
+procedure DFS(node r)
+   Stack S
+   S.push(r)
+   while not S.isEmpty()  do
+      u ← S.pop()
+       if u ≠ null then
+            visit(u)
+            S.push(right_child_of(u))
+            S.push(left_child_of(u))
+       fi
+   od
+end 
+```
+
+in a visit in depth we continue the visit from the last node left over
+since we stack first the right-hand child of each node and then the left-hand child we tend to follow all the left-hand children going deep until the first left-hand leaf is reached in general we will only visit each right-hand subtree in a node when the left-hand subtree has been visited altogether
+
+
+By reversing the order in which we add the children we have the symmetrical variant
+
+The recursive deep-visit version shown below is much more elegant:
+the stack does not appear explicitly in the algorithm as it is the stack of records for activating recursive calls to keep nodes open.
+
+There are the obvious variations if we alter the order of the visit and child addition instructions in the S-stack.
+* preorder visit = we visit the root first then left child and then right child
+* symmetrical visit = left first,then root and then right
+* visit in post order = first left,then right and finally root
+
+```
+-- DFS recursive visit
+procedure DFS(node r)
+     if r = null then return
+     visit(u)
+     DFS(left_child_of(r))
+     DFS(right_child_of(r))
+end
+```

…/2014-10-14-breadth-first-search-tree.md …/2014-10-14-breadth-first-search-tree.md_posts/it/computer-science/2014-10-14-breadth-first-search-tree.md renamed to _posts/en/computer-science/data-structures-and-algorithms/2014-10-14-breadth-first-search-tree.md _posts/it/computer-science/2014-10-14-breadth-first-search-tree.md renamed to _posts/en/computer-science/data-structures-and-algorithms/2014-10-14-breadth-first-search-tree.md

@@ -1,24 +1,24 @@
 ---
 layout: post
-lang: it
-title:  "Algoritmo di visita in ampiezza per un albero (BFS o Breadth First Search)"
+lang: en
+title:  "BFS Breadth First Search"
 excerpt: ""
-category: "Computer Science"
+category: data-structures-and-algorithms
 date:   2014-10-14 22:45:33
-tags: [Italian,"Data Structures","Algorithms"]
+tags: [English,"Algorithms"]
 comments: true
 share: true
 ---
 
 > "PHP is a minor evil perpetrated and created by incompetent amateurs, whereas Perl is a great and insidious evil perpetrated by skilled but perverted professionals."
 (Jon Ribbens)
 
-Partiamo dal algoritmo di generico di visita e usando una coda per rappresentare `S` otteniamo visita in ampiezza (Breadth First Search o BFS).
+We start with the generic visitation algorithm and using a queue to represent `S` we obtain breadth-first visitation (Breadth First Search or BFS).
 
-I nodi vengono visitati per livelli,prima radice,poi figli della radice,poi i figli dei figli.
+Nodes are visited by levels, first root, then children of the root, then children of the children.
 
-```java
-proc BFS(nodo r)
+```
+proc BFS(node r)
    Queue C
    C.enqueue(r)
    while not C.isEmpty() do 
@@ -29,5 +29,5 @@ proc BFS(nodo r)
            C.enqueue(right_child_of(u))
        fi	
    od
-endproc 
-```
+end proc 
+```

…oblem-when-restoring-a-mysql-database.md …oblem-when-restoring-a-mysql-database.md_posts/en/computer-science/databases/2010-01-01-mysql-a-problem-when-restoring-a-mysql-database.md renamed to _posts/en/computer-science/databases/mysql/2010-01-01-mysql-a-problem-when-restoring-a-mysql-database.md _posts/en/computer-science/databases/2010-01-01-mysql-a-problem-when-restoring-a-mysql-database.md renamed to _posts/en/computer-science/databases/mysql/2010-01-01-mysql-a-problem-when-restoring-a-mysql-database.md

@@ -8,7 +8,6 @@ date:   2011-02-03 22:45:33
 tags: [English,MySQL]
 comments: true
 share: true
-permalink: /databases/mysql/2
 ---
 We download the tar.gz from the mysql site www.mysql.com
 place it in /usr/local unpack the tar.gz with the command

…u-linux-mysql-without-synaptic-or-apt.md …u-linux-mysql-without-synaptic-or-apt.md_posts/en/computer-science/databases/2010-02-03-mysql-how-to-install-on-ubuntu-linux-mysql-without-synaptic-or-apt.md renamed to _posts/en/computer-science/databases/mysql/2010-02-03-mysql-how-to-install-on-ubuntu-linux-mysql-without-synaptic-or-apt.md _posts/en/computer-science/databases/2010-02-03-mysql-how-to-install-on-ubuntu-linux-mysql-without-synaptic-or-apt.md renamed to _posts/en/computer-science/databases/mysql/2010-02-03-mysql-how-to-install-on-ubuntu-linux-mysql-without-synaptic-or-apt.md

@@ -9,7 +9,6 @@ modified: 2017-05-03 22:45:33
 tags: [English,MySQL]
 comments: true
 share: true
-permalink: /databases/mysql/3
 ---
 Well, you probably have different collations in some mysql views, try to force collation like this 
 ```sql  

…1-01-mysql-mysqldump-speed-up-restore.md …1-01-mysql-mysqldump-speed-up-restore.md_posts/en/computer-science/databases/2011-01-01-mysql-mysqldump-speed-up-restore.md renamed to _posts/en/computer-science/databases/mysql/2011-01-01-mysql-mysqldump-speed-up-restore.md _posts/en/computer-science/databases/2011-01-01-mysql-mysqldump-speed-up-restore.md renamed to _posts/en/computer-science/databases/mysql/2011-01-01-mysql-mysqldump-speed-up-restore.md

@@ -3,10 +3,10 @@ layout: post
 lang: en
 title: "The Data Model Resource Book Vol 1 & 2"
 excerpt: "About the series book..."
-category: data-science
+category: data-engineering
 date: 2023-05-06 05:11:55
 modified: 2023-05-13 05:11:55
-tags: [English,"Data Science","Data Engineering"]
+tags: [English,"Data Engineering"]
 comments: true
 share: true
 updated: true