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🧠 AI
👨‍💻 Software
⚾ Soft Skill
🧮 Mathematics

Ehsan Ahmadi's Personal Docs

🚀 Cheatsheet
🚀 Cheatsheet
- AI
- CI/CD
- Docker
- .Net
- Elasticsearch
- Git
- Go
- Linux
- MongoDB
- Mysql
- Neo4j
- Nginx
- Postgresql
- Python
- Rabbitmq
- 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
- 📈 Charts
  📈 Charts
- 🧮 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
    
    MAE
    
    MSE
    
    Coefficient of Determination (\(R^2\))
    
    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}
    
    KNN * [Sup] [Multilabel]
    
    Label Propagation [Semi-Sup]
    
    Logistic Regression * [Sup] {Multinomial}
    
    Naive Bayes [Sup]
    
    SVM [Sup]
    
    Vs (SVM & Logistic Regression)
  - Clustering
    Clustering
    
    Overview
    
    Anomaly [Unsup]
    
    HDBSCAN * [Unsup] [Density-Based]
    
    K-Means * [Unsup] [Centroid-Based]
    
    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} {Swish}
    
    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 *
    
    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}
  - Compression
    Compression
    
    Overview
    
    Knowledge Distillation
    
    Pruning {Magnitude-Based} {L2-Norm} {Iterative}
    
    Quantization {QAT} {PTQ} {Mixed-Precision} {Dynamic}
  - Interpretability
    Interpretability
    
    Overview
    
    Attention Visualization [NLP]
    
    Integrated Gradients [NLP] [Attribution]
    
    Mechanistic Interpretability (MI) [NLP]
    
    Probing [NLP]
  - Memory efficiency
    Memory efficiency
    
    Overview
    
    Gradient Checkpointing
    
    Mini-Batch
  - Optimization
    Optimization
    
    Overview
    
    Batch Normalization (BN)
    
    Gradient Clipping
    
    Layer Normalization (LN) *
    
    Learning Rate Decay * {1cycle}
    
    Optimizers * {Adam} {AdamW} {SGD}
    
    Sharpness-Aware Minimization (SAM)
    
    Weight Initialization *
  - Regularization
    Regularization
    
    Overview
    
    Dropout *
    
    Freezing Layers *
    
    Noise Injection {FGSM} {DP}
    
    Stochastic Weight Averaging (SWA)
  - 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
  - Training
    Training
    
    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]
    
    SFT * [Sup]
- 🗣️ 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 * temperature *
    Table of contents
    
    Description
    
    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
- Evaluation
  Evaluation
- Regularization
  Regularization
👨‍💻 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
- 📢 Protocols
  📢 Protocols
- 🗃️ 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
- 🥞 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
🧮 Mathematics
🧮 Mathematics
- Fundamentals
  Fundamentals
  - Functions
  - Logic
  - Math Idioms
  - Sets
- Linear algebra
  Linear algebra
  - Matrix
  - Subset
  - Subspace
  - Transformation
  - Vector
- Probability and statistics
  Probability and statistics

Table of contents

Description

temperature *

Description

A higher temperature increases the likelihood that less probable tokens are generated and vice versa.

©2020-2025 Ehsan Ahmadi