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Loosely Coupled Principle
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Ehsan Ahmadi's Personal Docs
🚀 Cheatsheet
🚀 Cheatsheet
CI/CD
Data Science / AI
Docker
.Net
Elasticsearch
Git
Go
Kubernetes
Linux
MongoDB
Mysql
Nginx
Postgresql
Python
Rabbitmq
Wordpress
🧪 Data Science
🧪 Data Science
Overview
Overview
9 Laws
Funny Topics
Projects Flow
🔍 Features & Data
🔍 Features & Data
Data cleaning
Data cleaning
Handling Imbalanced Data
Handling Missing Values
Handling Noisy Data
Handling Outliers
Removing Duplicates
Data sampling
Data sampling
Overview
Importance Sampling
Reservoir Sampling
Simple Random Sampling
Stratified Sampling
Weighted Sampling
Data size
Data size
Overview
Aggregation
Augmentation
Clustering
Compression
Generalization
Feature engineering extraction
Feature engineering extraction
Overview
Construction
Construction
Interaction Terms
Polynomial Expansion
Encoding
Encoding
Bin-Counting
Dummy Coding
Effect Coding
Feature Hashing
One-Hot Encoding
TF-IDF
Transforming Nominal Attributes
Transforming Ordinal Attributes
Scaling
Scaling
Log Transformation
Mean Normalization
Normalization (Min-Max)
Power Transformation [Box-Cox]
Robust Scaling
Standardization (Z-score)
Vs (Normalization & Standardization)
Transformation
Transformation
Binarization
Binning (Quantization)
Rounding
Feature selection reduction
Feature selection reduction
Cheatsheet
Overview
Dimensionality reduction linear
Dimensionality reduction linear
Overview
Factor Analysis
Linear Discriminant Analysis (LDA)
Principal Component Analysis (PCA)
Vs (PCA & LDA)
Dimensionality reduction manifold
Dimensionality reduction manifold
Overview
Hessian Eigenmapping (HLLE)
Isometric Feature Mapping (Isomap)
Locally Linear Embedding (LLE)
Multi-dimensional Scaling (MDS)
Spectral Embedding (Laplacian Eigenmaps)
T-Distributed Stochastic Neighbor Embedding (T-SNE)
Uniform Manifold Approximation and Projection (UMAP)
Filter
Filter
Cheatsheet
Overview
ANOVA [Normal] [Continuous vs Categorical]
Chi-Squared [Not Normal] [2 Categorical]
Mutual Information (Information Gain) [Not Normal] [Continuous or Categorical]
Pearson Correlation Coefficient (R) [Normal] [2 Continuous]
Point-Biserial Correlation Coefficient (Rpb) [Normal] [Continuous vs Binary]
Spearman Correlation Coefficient (\(Rs\) or \(\rho\)) [Not Normal] [2 Continuous]
T-Test [Normal] [Continuous vs Categorical]
Variance Threshold [Not Normal] [1 Continuous]
Wrapper
Wrapper
Overview
Recursive Feature Elimination (RFE)
🧠 AI
🧠 AI
🦾 Machine Learning
🦾 Machine Learning
Overview
Classification
Classification
Decision Trees [Tree]
K-Nearest Neighbors (KNN)
Logistic Regression
Multinomial Logistic Regression
Naive Bayes
Random Forest [Tree]
Support Vector Machine (SVM)
XGBoost (eXtreme Gradient Boosting) [Tree]
Share (Information Gain Technique) [Tree]
Vs (SVM & Logistic Regression)
Clustering
Clustering
Overview
Anomaly
HDBSCAN
K-Means
VADER [Sentiment Analysis]
Feature extraction
Feature extraction
FastText
Word2Vec
Recommender
Recommender
Collaborative Filtering [Unsup]
Content-Based Filtering [Unsup]
Simple Recommendation [Unsup]
Regression
Regression
Linear Regression (Least Squares) [Sup]
Polynomial Regression [Sup]
Reinforcement
Reinforcement
Reinforcement Learning [Unsup]
Topic modeling
Topic modeling
Overview
BERTopic
LDA
NMF
🤖 Neural Network
🤖 Neural Network
Activation functions
Activation functions
Linear [\(-\infty\) to \(+\infty\)]
ReLU [\(0\) to \(+\infty\)]
Sigmoid [0 to 1]
Softmax [0 to 1]
Softplus [\(0\) to \(+\infty\)]
TanH [-1 to 1]
Threshold [0 or 1]
Cost functions
Cost functions
Overview
Cosine Similarity [Embedding]
Cross-Entropy Loss [Multi-Class Classification]
Distortion [Signal Processing]
Log Loss [Binary Classification]
Multiple Negative Ranking (MNR) (InfoNCE7) (NTXentLoss) [Embedding]
Squared Error Loss [Regression]
Fine tuning
Fine tuning
Overview
Contrastive Learning
Freezing Layers
LoRA [QLoRA]
Masked Language Modeling (MLM)
Parameter-Efficient Fine-Tuning (PEFT)
SetFit
Learning rate techniques
Learning rate techniques
Adam Algorithm
ADOPT Algorithm
Batch & Mini-batch
Gradient Descent With Momentum
Hyperparameters Tuning
Learning Rate Decay (lrDecay)
Weight Initialization
Network types
Network types
Auto-Encoders (AE) [Dimensionality Reduction]
Bi-Encoders (Dense Retrieval)
Convolutional (CNN)
Cross-Encoders (Reranking)
Feed Forward (FFNN)
Generative Adversarial (GAN) [Data Augmentation]
Mamba [NLP]
Recurrent (RNN) [Sequential Data]
RWKV [NLP]
Time Series Models [Correlated Data]
Transformers [NLP]
Tasks
Tasks
Fill-Mask
Named Entity Recognition (NER)
Neural Machine Translation
Part of Speech Tagging (POS)
Question Answering
Sentiment Analysis
Text Generation
Text Summarization
Zero-Shot Classification
🗣️ LLM
🗣️ LLM
Chain types
Chain types
Map-Reduce
Map-Rerank
Refine
Search Type
Stuffing
Hyperparameters
Hyperparameters
max_token
Presence Penalty
Quantization
Repetition Penalty
temperature
top_k
top_p
Vs (top_p & temperature)
Jailbreaking
Jailbreaking
Overview
Encoding
Many-Shot
Prompt Iinjection
Prompting
Prompting
Chain-of-Thought (CoT)
Few-Shot
Generated Knowledge
Meta
Prompt Chaining
Retrieval Augmented Generation (RAG)
Self-Consistency
Structured
Tree-of-Thoughts (ToT)
Zero-Shot
Zero-Shot Chain-of-Thought
Vs (Generated Knowledge & RAG)
Vs (Meta & Few-Shot)
Tokenization
Tokenization
Outputs
Outputs
Byte Tokens
Character Tokens
Subword Tokens
Word Tokens
Strategies
Strategies
Byte Pair Encoding (BPE)
SentencePiece
WordPiece
Evaluation
Evaluation
Data Distribution
Orthogonalization
Fitting problems
Fitting problems
Overview
Ensemble methods
Ensemble methods
Overview
Bagging (Bootstrap Aggregating)
Boosting
Gradient Boosting
Stacking
For neural networks
For neural networks
Bigger Neural Network
Dropout Regularization
Early Stopping Regularization
Hyperparameter tuning
Hyperparameter tuning
Training CV
Imbalanced data
Imbalanced data
Cost-Sensitive Learning
Regularization
Regularization
Overview
LASSO (L1)
Ridge (L2)
Vs (LASSO & Ridge)
Splitting data
Splitting data
Holdout Method (Train/Test Split)
K-Fold
Leave-P-Out
Stratified K-Fold
Time Series Cross-Validation
Learning types
Learning types
Overview
Autoregressive Language Modeling [Self-Sup]
Co-Training [Semi-Sup]
Label Propagation [Semi-Sup]
Masked Language Modeling (MLM) [Self-Sup]
📈 Charts
📈 Charts
Categorical
Categorical
Bar
Confusion Matrix (Error Matrix)
Receiver Operator Characteristic (ROC)
Continuous
Continuous
Histogram
Scatter
⚖️ Metrics & Evaluation
⚖️ Metrics & Evaluation
Categorical
Categorical
Accuracy [Balance]
F1 [Imbalance]
Precision (PPV) [Imbalance]
Recall (TPR) [Imbalance]
Vs (Recall & Precision)
Continuous
Continuous
Mean Absolute Error (MAE)
Mean Squared Error (MSE)
Coefficient of Determination (\(R^2\))
Root Mean Squared Error (RMSE)
Vs (MSE & RMSE)
👨💻 Software
👨💻 Software
📜 Languages
📜 Languages
Overview
Overview
Programming Idioms
C#
C#
Architecture
Architecture
Architecture
Memory Management
Namespace
Solution & Project
Asp
Asp
Area
Background Tasks
Controller
Data annotations
DI
Identity
Routing
Structure
View
Class
Class
Class
Delegate & Event
Enumeration
Field
Interface
Method
Property
Record
Structure
Collections
Collections
Overview
Array
ArrayList
Dictionary
Hashset
Hashtable
LINQ
List
Queue
SortedList
Stack
Share (Dictionary & SortedList & Hashtable)
Share (List & Hashset & ArrayList & Stack & Queue)
Ef
Ef
Overview & Installation
CMD
Command & Query
DB Context & DB Set
Model & Fields
Fundamentals
Fundamentals
Async & Await
Character Encoding
Command Line Arguments
Control Statements
Data Types
DateTime
dotnet CLI
Exception Handling
IO
Keywords
Math
Operators
Serialization
String
Go
Go
Passed by Value/Reference
Rust
Rust
Ownership
String
Uml
Uml
Overview
Aggregation (Constructor List)
Association (Constructor Access)
Composition (Constructor Create)
Dependency (Method Access)
Generalization (Extend)
Realization (Implementation)
👷 DevOps
👷 DevOps
AWS Services
DNS
FASS
Github Actions
Linux
📢 Protocols
📢 Protocols
Authentication protocols
Authentication protocols
Oauth
OAuth2 [OIDC]
SAML
Message brockers
Message brockers
Kafka
Protocol rules
Protocol rules
Resource Naming
RESTful API [HTTP1]
Underlying protocols
Underlying protocols
gRPC [HTTP2]
OSI Model
WebRTC [UDP]
WebSocket [TCP]
🗃️ Storages & Data
🗃️ Storages & Data
overview
overview
Glossary
Storage Estimation
Storage Selection
Cache
Cache
Cache Types
System Memory Hierarchy
Data structures
Data structures
Overview
Array
AVL Tree (Balanced Binary Search Tree)
B-Tree (Balanced Tree)
Binary Search Tree
Binary Tree
Bloom Filter
Characters
Graph
Hash Table (Hash Map)
Heap
Linked List
Queue
Red-Black Tree (Balanced Binary Search Tree)
Set
Skip List
Stack
Trie
Vs (Array & Linked List)
Db
Db
overview
overview
DB Types
DB Use Cases
Acid transaction
Acid transaction
Atomicity
Consistency
Durability
Isolation
Data warehouse
Data warehouse
Overview
Data Lake
Data Mart
Data Mesh
Data Pipeline
Data Warehouse
Delta Lake
Star Schema
Nosql
Nosql
Data Locality
LSM-Tree Storeage Structure
Elasticsearch
Elasticsearch
Glossary
DSL (Query Clauses)
DSL (Query Contexts)
Mapping
Replication
Replication
Overview
Approach: Leaderless Replication
Approach: Multi-Leader Replication
Approach: Single-Leader Replication
Replication Lag Problem
Vs (Single-Leader & Multi-Leader)
Sql
Sql
B-Tree Storeage Structure
Concurrency Control
Row-Based vs Column-Based
SQL Commands
Standard/Virtual View
Log and metric
Log and metric
Logging
Metrics
Tracing
Vs (Logging & Tracing & Metrics)
Object storages
Object storages
Ceph
MinIO
System design
System design
Algorithms and patterns
Algorithms and patterns
API Gateway
Bulkhead
Circuit Breaker
CQRS
Event-Driven
Outbox
Saga
Service Mesh
Service Registry
Sidecar
Strangler
Architectures
Architectures
Overview
Clean Architecture
DDD
Hexagonal Architecture
Micro Services
Maintainability
Maintainability
Overview
Evolvability
Operability
Simplicity
Reliability
Reliability
Overview
Fault-Tolerant (Resilient)
Scalability
Scalability
Overview
Points
System development
System development
OOP
OOP
Concepts
Concepts
Abstraction
Encapsulation
Inheritance
Polymorphism
Design patterns
Design patterns
Overview
Adapter (Interface compatibility bridge) [Structural]
Bridge (Decoupled abstraction) [Structural]
Builder (Step-by-step construction) [Creational]
Chain of Responsibility (Request delegation chain) [Behavioral]
Command (Encapsulated request object) [Behavioral]
Composite (Tree-like structure) [Structural]
Decorator (Dynamic object enhancement) [Structural]
Facade (Simplified interface) [Structural]
Factory (Object creation delegation) [Creational]
Flyweight (Shared object reuse) [Structural]
Interpreter (Language parsing engine) [Behavioral]
Iterator (Sequential access) [Behavioral]
Mediator (Centralized communication) [Behavioral]
Memento (State preservation) [Behavioral]
Observer (Event notification system) [Behavioral]
Prototype (Object cloning) [Creational]
Proxy (Controlled access) [Structural]
Singleton (Single instance control) [Creational]
State (State-specific behavior) [Behavioral]
Strategy (Flexible algorithm selection) [Behavioral]
Template Method (Template with hooks) [Behavioral]
Visitor (Element-specific operations) [Behavioral]
Vs (Adapter & Facade)
Vs (Builder & Factory)
Vs (Decorator & Proxy)
Vs (Template Method & Factory Method & Strategy)
Principles
Principles
Overview
Favour Composition Over Inheritance Principle
Hollywood Principle
Least Knowledge Principle
Loosely Coupled Principle
Loosely Coupled Principle
Table of contents
Description
Open Close Principle
Program to Interface Principle
Safety vs Transparency
Separate What Vary Principle
Single Responsibility Principle
🛡️ Security
🛡️ Security
Bearer Token Authentication
MD5 & SHA
OWASP
PASETO Token
Session Management (Access/Refresh Token)
Symmetric & Asymmetric Encryption
Testing
Testing
API Test
Typing
Typing
Duck Typing
Dynamic & Static Typing
Goose Typing
Static Duck Typing (Structural Subtyping)
Version control
Version control
Micro Commit
⚾ Soft Skill
⚾ Soft Skill
Google's Keys to Successful Teams
Great Managers Book
Personality Types
🧮 Mathematics
🧮 Mathematics
Fundamentals
Fundamentals
Functions
Logic
Math Idioms
Sets
Linear algebra
Linear algebra
Matrix
Subset
Subspace
Transformation
Vector
Probability theory and statistics
Probability theory and statistics
Central Limit Theorem
Covariance
Degrees of Freedom
Expectation
Pearson Correlation Coefficient (\(R\))
Quantile [Percentile, Decile, Quartile]
Standard Deviation
Variance
Vs (Correlation & Covariance)
Vs (Variance & Standard Deviation)
Table of contents
Description
Loosely Coupled Principle
Description
