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Ehsan Ahmadi's Personal Docs
🚀 Cheatsheet
🚀 Cheatsheet
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Table of contents
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Wordpress
🧪 Data Science
🧪 Data Science
Overview
Overview
9 Laws
Projects Flow
🗃️ Data
🗃️ Data
Cleaning
Cleaning
Cheatsheet
Duplicates {LSH} {Shingling}
Imbalanced Data {SMOTE} {NearMiss}
Missing Values
Noisy Data
Outliers
Creation
Creation
Augmentation
Distribution types
Distribution types
Binomial
Long Tail
Normal [Bell Curve] [Gaussian]
Poisson
Uniform
Reduction
Reduction
Overview
Aggregation
Clustering
Compression
Generalization
Sampling {Stratified}
🔍 Feature
🔍 Feature
Creation
Creation
Overview
Encoding {One-Hot} {TF-IDF}
Interaction Terms
Polynomial Expansion
Reduction
Reduction
Overview
Factor Analysis [Linear]
Hessian Eigenmapping (HLLE) [Manifold]
Isometric Feature Mapping (Isomap) [Manifold]
Linear Discriminant Analysis (LDA) [Linear]
Locally Linear Embedding (LLE) [Manifold]
Multi-dimensional Scaling (MDS) [Manifold]
Principal Component Analysis (PCA) [Linear]
Recursive Feature Elimination (RFE) [Wrapper]
Spectral Embedding (Laplacian Eigenmaps) [Manifold]
T-Distributed Stochastic Neighbor Embedding (T-SNE) [Manifold]
Uniform Manifold Approximation and Projection (UMAP) [Manifold]
Vs (PCA & LDA)
Relation
Relation
Cheatsheet
Overview
ANOVA [Continuous vs Categorical] [Normal]
Chi-Squared [2 Categorical] [Not Normal]
Mutual Information (Information Gain) [Continuous or Categorical] [Not Normal]
Pearson Correlation Coefficient (R) [2 Continuous] [Normal]
Point-Biserial Correlation Coefficient (Rpb) [Continuous vs Binary] [Normal]
Spearman Correlation Coefficient (\(Rs\) or \(\rho\)) [2 Continuous] [Not Normal]
T-Test [Continuous vs Categorical] [Normal]
Variance Threshold [1 Continuous] [Not Normal]
Transformation
Transformation
Binarization
Binning (Quantization)
Log Transformation [Scaling]
Mean Normalization [Scaling]
Normalization (Min-Max) [Scaling]
Power Transformation [Scaling] {Box-Cox}
Robust Scaling [Scaling]
Rounding
Standardization (Z-score) [Scaling]
Vs (Normalization & Standardization)
📈 Charts
📈 Charts
Bar [Categorical]
Confusion Matrix (Error Matrix) [Categorical]
Histogram [Continuous]
Receiver Operator Characteristic (ROC) [Categorical]
Scatter [Continuous]
🧮 Metrics
🧮 Metrics
Annotation
Annotation
Cohen's Kappa [2 Annotators]
Fleiss' Kappa [+3 Annotators]
Classification
Classification
Accuracy [Balance]
AUC-ROC
F1 [Imbalance]
Precision (PPV) [Imbalance]
Recall (TPR) [Imbalance]
Vs (Recall & Precision)
Generation
Generation
BLEU [NLP]
Perplexity [NLP]
Quality Filtering [Augmentation]
ROUGE [NLP]
Semantic Similarity [Augmentation]
Word Error Rate (WER) [STT]
Regression
Regression
Mean Absolute Error (MAE)
Mean Squared Error (MSE)
Coefficient of Determination (\(R^2\))
Root Mean Squared Error (RMSE)
Vs (MSE & RMSE)
Retrieval
Retrieval
Mean Reciprocal Rank (MRR)
NDCG
🧠 AI
🧠 AI
🦾 Machine Learning
🦾 Machine Learning
Overview
Classification
Classification
Active learning [Sup]
Co-Training [Semi-Sup]
Decision Trees [Sup] {Random Forest} {XGBoost]}
K-Nearest Neighbors (KNN) [Sup]
Label Propagation [Semi-Sup]
Logistic Regression [Sup]
Multinomial Logistic Regression [Sup]
Naive Bayes [Sup]
Support Vector Machine (SVM) [Sup]
Vs (SVM & Logistic Regression)
Clustering
Clustering
Overview
Anomaly [Unsup]
HDBSCAN [Unsup]
K-Means [Unsup]
VADER [Unsup] [Sentiment Analysis]
Feature extraction
Feature extraction
Word2Vec [Self-Sup] {FastText}
Recommender
Recommender
Collaborative Filtering [Unsup]
Content-Based Filtering [Unsup]
Simple Recommendation [Unsup]
Regression
Regression
Linear Regression [Sup] {Polynomial}
Reinforcement
Reinforcement
Reinforcement Learning [Unsup]
Topic modeling
Topic modeling
Overview
BERTopic [Unsup]
LDA [Unsup]
NMF [Unsup]
🤖 Neural Network
🤖 Neural Network
Activation functions
Activation functions
Linear [\(-\infty\) to \(+\infty\)]
ReLU [\(0\) to \(+\infty\)] {Leaky} {ELU} {Softplus}
Sigmoid [0 to 1]
Softmax [0 to 1]
TanH [-1 to 1]
Threshold [0 or 1]
Architectures
Architectures
Auto-Encoders (AE) [Dimensionality Reduction]
Convolutional (CNN) [Image]
Feed Forward (FFNN)
Generative Adversarial (GAN) [Data Augmentation]
Mamba [NLP]
Parameter-Efficient Ensembling [Tabular]
Recurrent (RNN) [Sequential Data]
RWKV [NLP]
Time Series Models [Correlated Data]
Transformers [NLP] {MoE} {MoR}
Fine tuning
Fine tuning
Autoregressive Language Modeling [Self-Sup]
Catastrophic Forgetting [Sup] {EWC} {LwF}
Contrastive Learning [Self-Sup]
Masked Language Modeling (MLM) [Self-Sup]
PEFT [Sup] {Adapters} {LoRA}
RLHF [RL] {DPO} {Online RL}
SetFit [Self-Sup]
Supervised Fine-Tuning (SFT) [Sup]
Interpretability
Interpretability
Overview
Attention Visualization [NLP]
Integrated Gradients [NLP] [Attribution]
Mechanistic Interpretability (MI) [NLP]
Probing [NLP]
Optimization
Optimization
Knowledge Distillation
Pruning {Magnitude-Based} {L2-Norm} {Iterative}
Quantization {QAT} {PTQ} {Mixed-Precision} {Dynamic}
Regularization
Regularization
Adam {AdamW} {ADOPT}
Bigger Neural Network
Dropout
Early Stopping
Freezing Layers
Gradient Checkpointing
Gradient Descent With Momentum
Hyperparameters Tuning
Learning Rate Decay (lrDecay)
Lookahead Optimizer
Noise Injection {FGSM} {DP}
Sharpness-Aware Minimization (SAM)
Stochastic Gradient Descent (SGD) {Mini-Batch}
Stochastic Weight Averaging (SWA)
Weight Initialization
Tasks
Tasks
Bi-Encoders (Dense Retrieval)
Cross-Encoders (Reranking)
Fill-Mask
Named Entity Recognition (NER)
Natural Language Inference (NLI)
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
Embedding [RAG] {Sparse} {Dense}
Indexing Techniques [RAG] {HNSW} {IVF}
Map-Reduce
Map-Rerank
RAG
ReAct {ReWOO}
Refine
Reflection
Search Type
Stuffing
Hyperparameters
Hyperparameters
max_token
Presence Penalty
Repetition Penalty
temperature
top_k
top_p
Vs (top_p & temperature)
Jailbreaking
Jailbreaking
Overview
Encoding
Many-Shot
Prompt Injection
Prompting
Prompting
Chain-of-Thought (CoT)
Few-Shot
Generated Knowledge
Meta
Prompt Chaining
Self-Consistency
Step-Back
Structured
Tree-of-Thoughts (ToT)
Zero-Shot
Vs (Meta & Few-Shot)
Tokenization
Tokenization
Byte Tokens [Output]
Character Tokens [Output]
Subword Tokens {WordPiece} {BPE} {SentencePiece}
Word Tokens
🎯 Loss Functions
🎯 Loss 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]
Evaluation
Evaluation
Data Distribution
Loss Behavior Analysis {Divergence}
Orthogonalization
Fitting problems
Fitting problems
Overview
Ensemble Methods {Bagging} {Boosting}
Hyperparameter Tuning
Imbalanced Data
Regularization {L1 LASSO} {L2 Ridge}
Splitting Data {Holdout} {K-Fold}
👨💻 Software
👨💻 Software
📜 Languages
📜 Languages
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)
🧩 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
Open Close Principle
Program to Interface Principle
Safety vs Transparency
Separate What Vary Principle
Single Responsibility Principle
📢 Protocols
📢 Protocols
gRPC [HTTP2]
Oauth [Authentication]
OAuth2 [Authentication] {OIDC}
OSI Model
Resource Naming [Rule]
RESTful API [Rule] [HTTP1]
SAML [Authentication]
WebRTC [UDP]
WebSocket [TCP]
🗃️ Storage [DB] [Cache]
🗃️ Storage [DB] [Cache]
overview
overview
DB Types
DB Use Cases
Glossary
Storage Estimation
Storage Selection
Acid transaction
Acid transaction
Atomicity
Consistency
Durability
Isolation
Cache
Cache
Cache Types
System Memory Hierarchy
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
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
🌀 Algorithms
🌀 Algorithms
Searching
Searching
Overview
BFS & DFS [\(O(n)\)] [Brute-Force]
Binary Search [\(O(\log n)\)] [Adaptive]
Hash Search [\(O(1)\)] [Adaptive]
Linear Search [\(O(n)\)] [Brute-Force]
Tree Search [\(O(\log n)\)] [Adaptive]
Vs (Linear & Binary & Tree & Hash search)
Sorting
Sorting
Overview
Bubble Sort [\(O(n^2)\)] [Stable] [In-Place]
Bucket Sort [\(O(n + k)\)] [Stable] [Non-In-Place]
Counting Sort [\(O(n + m)\)] [Stable] [Non-In-Place]
Heap Sort [\(O(n \log n)\)] [Non-Stable] [In-Place]
Insertion Sort * [\(O(n^2)\)] [Stable] [In-Place]
Merge Sort [\(O(n \log n)\)] [Stable] [Non-In-Place]
Quick Sort * [\(O(n \log n)\)] [Non-Stable] [In-Place]
Radix Sort [\(O(nk)\)] [Stable] [Non-In-Place]
Selection Sort [\(O(n^2)\)] [Non-Stable] [In-Place]
Vs (Insertion & Quick Sort)
Vs (Quick & Merge & Heap Sort)
Vs (Selection & Bubble & Insertion Sort)
Strategies
Strategies
Backtracking [Top-Down] [Recursion]
Divide & Conquer [Top-Down] [Recursion]
Dynamic Programming [Bottom-Up] [Loop]
Greedy [Step-by-Step] [Loop]
🥞 Data Structures
🥞 Data Structures
Overview
Graph
Graph
Graph
Hashing
Hashing
Bloom Filter
Hash Table (Hash Map)
Linear
Linear
Array
Characters
Linked List
Queue
Stack
Vs (Array & Linked List)
Tree
Tree
AVL Tree (Balanced Binary Search Tree) [Binary Search Tree]
B-Tree (Balanced Tree)
Binary Search Tree [Binary Tree]
Binary Tree
Heap [Binary Tree]
Red-Black Tree (Balanced Binary Search Tree) [Binary Search Tree]
👷 DevOps
👷 DevOps
AWS Services
DNS
FASS
Github Actions
Linux
Applications
Applications
Kafka
Elasticsearch
Elasticsearch
Glossary
DSL (Query Clauses)
DSL (Query Contexts)
Mapping
System design
System design
Algorithms and patterns
Algorithms and patterns
API Gateway
Bulkhead
CAP Theorem
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
Overview
Overview
Programming Idioms
🛡️ Security
🛡️ Security
Bearer Token Authentication
MD5 & SHA
OWASP
PASETO Token
Session Management (Access/Refresh Token)
Symmetric & Asymmetric Encryption
Log and metric
Log and metric
Logging
Metrics
Tracing
Vs (Logging & Tracing & Metrics)
Testing
Testing
API Test
Typing
Typing
Duck Typing
Dynamic & Static Typing
Goose Typing
Static Duck Typing (Structural Subtyping)
Version control
Version control
Code Review Guide
Micro Commit
⚾ Soft Skill
⚾ Soft Skill
Atomic Habits Book
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 and statistics
Probability 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
Other
Rabbitmq
Other
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