vault backup: 2025-06-10 17:04:35

bionicop · bionicop · commit 3e092e4f6c95 · 2025-06-10T17:04:35.000+05:30
Number of files: M .gitignore content/1 - Indexes/Content Management System.md content/1 - Indexes/templates.md content/2 - Templates/Base Note.md content/6 - Base Notes/Algorithm Basics.md content/6 - Base Notes/cyber security.md content/6 - Base Notes/python cheat-sheet.md content/6 - Base Notes/question-bank.md, R content/1 - Indexes/AI and ML for Business Management.md content/1 - Indexes/IIIrd Semester.md, A content/5 - Source Material/@Active Notes/neural-networks_layers.png content/5 - Source Material/@Active Notes/neural-networks_weights-and-thresholds.png content/6 - Base Notes/AI Fundamentals.md content/6 - Base Notes/Machine Learning Types for Business.md Affected files: .gitignore content/1 - Indexes/AI and ML for Business Management.md content/1 - Indexes/Content Management System.md content/1 - Indexes/IIIrd Semester.md content/1 - Indexes/templates.md content/2 - Templates/Base Note.md content/5 - Source Material/@Active Notes/neural-networks_layers.png content/5 - Source Material/@Active Notes/neural-networks_weights-and-thresholds.png content/6 - Base Notes/AI Fundamentals.md content/6 - Base Notes/Algorithm Basics.md content/6 - Base Notes/Machine Learning Types for Business.md content/6 - Base Notes/cyber security.md content/6 - Base Notes/python cheat-sheet.md content/6 - Base Notes/question-bank.md
diff --git a/.gitignore b/.gitignore
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+.copilot-index
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diff --git a/content/1 - Indexes/AI and ML for Business Management.md b/content/1 - Indexes/AI and ML for Business Management.md
diff --git a/content/1 - Indexes/Content Management System.md b/content/1 - Indexes/Content Management System.md
@@ -0,0 +1,54 @@
+# Content Management System (CMS) Index
+
+Welcome to the CMS development guide! This systematic index covers CMS fundamentals and Drupal implementation.
+
+## 📚 Understanding CMS
+
+### Foundation Knowledge
+- **[[cms a brief introduction]]** - What is CMS and why use it
+  - CMS definition and benefits
+  - Popular CMS platforms (WordPress, Drupal, Joomla)
+  - Real-world examples and use cases
+
+### CMS Overview
+- **[[Drupal]]** - Deep dive into Drupal CMS
+  - Why choose Drupal
+  - Modular architecture benefits
+  - Scalability and security features
+
+## 🛠️ Drupal Development Journey
+
+### 1. **[[drupal project setup]]**
+- Prerequisites installation (WAMP/MAMP/XAMPP)
+- Composer setup
+- Database configuration
+- Initial Drupal installation
+- Basic website configuration
+
+### 2. **[[setting up drupal modules]]**
+- Essential modules overview
+- Module installation methods
+- Required modules for projects:
+  - Token, Pathauto, ctools
+  - Webform, Libraries API
+  - Views, Admin menu
+
+### 3. **[[setting up drupal's structure and it's content types]]**
+- Understanding Drupal structure
+- Content types creation
+- Custom fields management  
+- URL aliases configuration
+- Block management
+
+### 4. **[[setting up drupal's user roles]]**
+- Default user roles understanding
+- Creating custom roles (e.g., Agents)
+- Setting permissions
+- User account management
+
+## 🚀 Quick Start Guide
+
+1. **Begin with [[cms a brief introduction]]** - Understand CMS basics
+2. **Read [[Drupal]]** - Learn Drupal advantages
+3. **Follow [[drupal project setup]]** - Get hands-on experience
+4. **Continue with modules, structure, and user roles** - Build complete project
diff --git a/content/1 - Indexes/IIIrd Semester.md b/content/1 - Indexes/IIIrd Semester.md
diff --git a/content/1 - Indexes/templates.md b/content/1 - Indexes/templates.md
@@ -2,5 +2,6 @@
 
 # templates
 [[Base Note]]
+[[Index Note]]
 [[Books, Articles & Papers]]
 [[Video & Podcasts]]
diff --git a/content/2 - Templates/Base Note.md b/content/2 - Templates/Base Note.md
@@ -6,7 +6,6 @@ Tags:
 
 # {{Title}}
 Subject: 
-Topic: 
 
 
 
diff --git a/content/5 - Source Material/@Active Notes/neural-networks_layers.png b/content/5 - Source Material/@Active Notes/neural-networks_layers.png
diff --git a/content/5 - Source Material/@Active Notes/neural-networks_weights-and-thresholds.png b/content/5 - Source Material/@Active Notes/neural-networks_weights-and-thresholds.png
diff --git a/content/6 - Base Notes/AI Fundamentals.md b/content/6 - Base Notes/AI Fundamentals.md
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+2025-06-10 11:51
+
+Status: #complete 
+
+Tags: #AI-Fundamentals #ai
+
+# AI and ML for Business Management
+Subject: [[AI and ML for Business Management]]
+Lecture: 1 - 9th June 2025
+Semester: [[IIIrd Semester]]
+
+# Artificial Intelligence (AI)
+## What is AI?
+- AI systems learn from large amounts of data, identifying patterns and relationships that help make predictions and recommendations. They improve over time as they get exposed to new information.
+- AI system learns from large amount of data, Identifying patterns and relationship that helps make predictions and recommendations.
+- They improve overtime as they get exposed to new information. The goal is to match the capabilities of the human mind in an effort to automate the work and solve problems more efficiently.
+
+## Fundamentals of AI:
+- Through the combination of statistics and trial and error method machine learning algorithms identify relationship and patterns within large datasets enabling them to draw conclusions about new data.
+
+Now, that we have basic understanding of AI, let's explore how it can be applied in [[Machine Learning Types for Business|business management]].
diff --git a/content/6 - Base Notes/Algorithm Basics.md b/content/6 - Base Notes/Algorithm Basics.md
@@ -2,7 +2,7 @@
 
 Status: #ongoing
 
-Tags: [[What-is-algorithm]]
+Tags: #What-is-algorithm
 
 # Algorithm Basics
 Subject: [[Design and Analysis of Algorithms]]
diff --git a/content/6 - Base Notes/Machine Learning Types for Business.md b/content/6 - Base Notes/Machine Learning Types for Business.md
@@ -0,0 +1,123 @@
+2025-06-10 12:05
+
+Status: #complete 
+
+Tags: #machine-learning #ai #neural-networks
+
+# AI and ML for Business Management
+Subject: [[AI and ML for Business Management]]
+Lecture: 1 - 9th June 2025
+Semester: [[IIIrd Semester]]
+
+# Machine Learning Types
+## Supervised Learning (Labelled data; House Price Prediction)
+- It relies on datasets containing both ***input*** and ***output*** information enabling ML to make ***prediction***
+- **Business Use**: Predictive analytics, forecasting
+- Examples: Email spam detection, credit scoring, medical diagnosis.
+## Unsupervised Learning (Result type unknown; Text Classification)
+- Analyse unlabelled data allowing machine to ***discover the patterns and anomalies*** in large unstructured data sets that may have otherwise gone undetected by humans.
+- **Business Use**: Pattern discovery, market segmentation
+- Examples: Customer clustering, recommendation systems, fraud detection.
+## Semi-Supervised Learning (Categorized Unlabelled data; Custom Segmentation)
+- It's mix of both supervised and unsupervised learning where ***small amount of labelled data*** are processed ***alongside larger chunk of raw data***.
+- **Business Use**: Cost-effective analysis with limited labelled data
+- Examples: Image recognition, speech recognition.
+## Reinforcement Learning (Learn from previous data; Driverless Cars)
+- Sub-field of ML, that enables AI based system to *take actions in a dynamic environment* through ***trial and error methods*** to maximize the *collective rewards,* based on the feedback generated for respected actions.
+- **Business Use**: Dynamic optimization, automated decision-making
+- Examples: Gaming AI, trading algorithms, resource allocation.
+
+```mermaid
+flowchart TD
+	A[Business Problem] --> B{Data Availability}
+	B --> |Labeled Data| C[Supervised Learning]
+	B --> |Unlabeled Data| D[Unsupervised Learning]
+	B --> |Mixed Data| E[Semi-Supervised Learning]
+	B --> |Dynamic Environment| F[Reinforcement Learning]
+	
+	C --> G[Predictive Models]
+	D --> H[Pattern Discovery]
+	E --> I[Cost-Effective Analysis]
+	F --> J[Automated Optimization]
+```
+
+---
+# Neural Networks
+Neural Networks are **computational** systems, that make *decisions and prediction* in a way that is **similar to human brain**, processing information through *inter-connected nodes* called "Neurons".
+[![Neural Networks Layers](neural-networks_layers.png)](http://towardsdatascience.com/understanding-neural-networks-19020b758230/)
+These neurons are organized in layers.
+1. Input layer that receives **raw data**.
+2. One-or-More hidden layers where **information is processed** and **patterns are learned**.
+3. Output layer that generates a *final result*.
+
+Each Neuron in each layer has it's own associative **"weights"** and **"thresholds"**.
+[![Neural Networks Weights and Thresholds](neural-networks_weights-and-thresholds.png)](https://www.simplyblock.io/blog/image-recognition-with-neural-networks/)
+
+---
+# Fundamentals
+## Deep Learning
+- It is a sub-field of ML that teaches computers to ***process large quantity of unstructured data***
+- Using multi-layer structure of Neurons called *Neural Networks*. Deep learning system can recognize complex patterns in "Text, Images, Audios" and other forms of data to produce accurate insights.
+
+## Generative AI
+- Is an application of AI, It refers to a model that creates new content by identifying patterns within massive amount of **annotated data**. Given a prompt or input a model is able to draw a what it has learned to generate relevant, original works in real time.
+
+## Natural Language Process (NLP) 
+- It enables computers to understand, interpret and respond to human language it works by breaking down text into elements that a computer can analyse (Words, grammar, context) and identify patterns such as sentence structures and word association.
+
+|                      **Artificial Intelligence**                      |                              **Machine Learning**                              |
+| :-------------------------------------------------------------------: | :----------------------------------------------------------------------------: |
+|                  AI has ability to apply knowledge.                   |                          ML means gaining knowledge.                           |
+| Goal isn't accuracy, but to increase the chance of business sucesses. |                         Goal is to increase accuracy.                          |
+|               Creates system that simulate human mind.                |      Helps machine to learn from data to improve accuracy of prediction.       |
+|   AI systems may need rule setting and manual tuning in some cases.   |                   ML focuses on reducing human intervention.                   |
+|            AI system implementation is a complex process.             | ML implementation invloves a few steps and continues improvement of the model. |
+|                    AI system is a decision  maker.                    |            ML enables the system to learn new things from the data.            |
+
+```mermaid
+flowchart TD
+    A["Types of AI"] --> B["Based on Capabilities"]
+    A --> C["Based on Functionalities"]
+
+    B --> B1["Narrow AI"] --- BB1["*ex: Alexa*"]
+    B --> B2["General AI"] --- BB2["*ex: Computational Robot*"]
+    B --> B3["Super AI"]
+
+    C --> C1["Reactive Machine"] --- CC1["*ex: Robot Chess*"]
+    C --> C2["Limited Memory"] --- CC2["*ex: Driverless Car*"]
+    C --> C3["Theory of Mind"] --- CC3["*ex: Robot mimic emotions*"]
+    C --> C4["Self-Awareness"] --- CC4["*ex: Robot has own emotions*"]
+
+```
+
+### Based on Capabilities 
+#### Narrow AI
+- Also know as "**Weak AI**", focuses on limited tasks and can not preform big problems.
+- It targets a single subset of cognitive abilities and advances in that area.
+- Example: Alexa by Amazon.
+#### General AI
+- Also knows as "**Strong AI**" can understand and 
+- It allows a machine to apply knowledge and skills in different context.
+- Example: [Fujitsu built K computer which is the fastest computer.](https://en.wikipedia.org/wiki/K_computer)
+#### Super AI
+- It surpasses human intelligence, and can perform any tasks better than a human.
+- Some of the critical aspects of the Super AI includes "Thinking, solving puzzles, making judgments".
+- Example: R2-D2 from the movie Star War which could perform multiple technical operations beyond human.
+
+### Based on Functionalities
+#### Reactive Machine
+- It is a primary form of AI that doesn't store memory or use past experience to determine future actions.
+- It works only with the present data.
+- Reactive machines are provided with specific tasks and they don't have capabilities beyond those tasks.
+- Example: [IBM deep blue that defeated the world chess champion, Garry Kasparov](https://www.ibm.com/history/deep-blue)
+#### Limited Memory
+- It trains from past data to make decisions. The memory of thus system is short lived.
+- They can use this past data for specific duration of time.
+- This kind of technology is used in self driving vehicles.
+#### Theory of Mind
+- It represents a advance class of technology, such kind of AI requires through understanding that the people and things within an environment can alter feelings and behaviour.
+- Example: [Kismet is a robot developed by MIT that can simulate and recognize human emotions.](https://en.wikipedia.org/wiki/Kismet_(robot))
+#### Self Awareness
+- It only exists hypothetically \*YET\*.
+- Such system understands they are internal traits states, conditions and perceive human emotions.
+- This type of AI will not only be able to understand and evoke emotions in those they interact with, but also have emotions, needs, and believes of their own. 
diff --git a/content/6 - Base Notes/cyber security.md b/content/6 - Base Notes/cyber security.md
@@ -26,9 +26,9 @@ graph TD
 
 > 📝 **Exam Tip**: Be prepared to explain each component of the CIA triad with a real-world example, preferably in an Indian context.
 
-### 1.2 Threat Modeling
+### 1.2 Threat Modelling
 
-Threat modeling is a proactive approach to identifying potential security risks:
+Threat modelling is a proactive approach to identifying potential security risks:
 
 ```mermaid
 graph LR
@@ -40,7 +40,7 @@ graph LR
     F --> A
 ```
 
-> 💡 **Real-world Application**: Consider how CERT-In (Indian Computer Emergency Response Team) might use threat modeling to protect critical infrastructure.
+> 💡 **Real-world Application**: Consider how CERT-In (Indian Computer Emergency Response Team) might use threat modelling to protect critical infrastructure.
 
 ### 1.3 Cyber Attack Trends
 
diff --git a/content/6 - Base Notes/python cheat-sheet.md b/content/6 - Base Notes/python cheat-sheet.md
@@ -1,8 +1,12 @@
-Here's a **systematic cheat sheet in table format**, along with the specific solutions to your assignments as patterns below. The table summarizes patterns in list manipulations, dictionaries, strings, sets, and functions, which can be used in various scenarios by changing or adjusting the input slightly.
+2025-06-10 12:35
 
----
+Status: #complete 
+
+Tags: #python-cheatsheet #python #programming-basics 
 
-## **Python Cheat Sheet - Key Patterns and Syntax**
+# python cheatsheet
+Subject: [[Introduction to Python]]
+# **Python Cheat Sheet - Key Patterns and Syntax**
 
 | **Category**               | **Task**                             | **Syntax / Example**                                                           | **Explanation**                                                     |
 | -------------------------- | ------------------------------------ | ------------------------------------------------------------------------------ | ------------------------------------------------------------------- |
diff --git a/content/6 - Base Notes/question-bank.md b/content/6 - Base Notes/question-bank.md
@@ -1,3 +1,4 @@
+Subject: [[Content Management System]]
 # Course: Design of Content Management System
 *Max: 80 Marks*
 

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