Haobin Tan
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    • AI
      • ML
        • ML Fundamentals
          • Math Basics
          • e2e ML Project
          • Evaluation
          • ML Algo overview
        • Model Selection
          • Objective Function
          • Bias Variance Tradeoff
          • Cross Validation
        • Regression
          • Linear Regression
          • Polynomial Regression
          • Kernelized Ridge Regression
        • Classification
          • K Nearest Neighbors
          • Logistic Regression: Basics
          • Logistic Regression: Probabilistic view
          • SVM: Basics
          • SVM: Kernel Methods
          • SVM: Kernelized SVM
        • Decision Trees
          • CART
        • Ensemble Learning
          • Why ensemble learning?
          • Voting Classifier
          • Random Forest
          • Ensemble Learners
          • Boosting
            • Bagging and Pasting
            • AdaBoost
          • Non-parametric
            • Linear Discriminant Functions
            • LDA
          • Unsupervised Learning
            • Gaussian Mixture Model
            • PCA
        • DL
          • NN Basics
            • Perceptron
            • 👍 Activation Functions
            • 👍 Loss Functions
            • MLP and Backprop
            • Math: Softmax
            • Generalization
            • Generalization: Dropout
            • 👍 Generalization: Data Augmentation
          • Efficient Training
            • Optimizers
            • 👍 Batch Normalization
          • Unsupervised Learning
            • Auto Encoder
            • Hopfield Nets
            • Bolzmann Machine
            • Restricted Boltzmann Machines
          • CNN
            • TDNN
            • 👍 CNN Basics
            • 👍 CNN Intuition and Visualization
            • CNN History
            • Computer Vision
            • Resources
          • Parallelism
            • Parallelism and Vectorization
          • RNN
            • Recurrent Neural Networks
            • LSTM
            • 👍 RNN Summary
            • 👍 LSTM Summary
            • BPTT
            • RNN Implementation
            • RNN Resource
          • Encoder-Decoder
            • Seq2Seq
            • 👍 Attention
            • 👍 Transformer
        • CV
          • CV Lecture
            • Pattern Recognition
            • Face Detection: Color-Based
            • Face Detection: Neural-Network-Based
            • Face Recognition: Traditional Approaches
            • Face Recognition: Features
            • Face Recognition: Deep Learning
            • Facial Feature Detection
            • Facial Expression Recognition
            • People Detection: Global Approaches
            • People Detection: Part-based Approaches
            • People Detection: Deep Learning Approaches
            • Tracking
            • Tracking 2
            • Body Pose
            • Gesture Recognition
            • Action & Activity Recognition
            • Action & Activity Recognition 2
          • Segmentation
            • Semantic Segmentation Overview
            • Semantic Segmentation with PyTorch
          • Face
            • Modern Face Recognition Overview
            • Eigenface
          • Visual Transformer
            • Attention Mechanism
            • Transformer
            • Visual Transformer
          • HPE
            • HPE Datasets
          • Object Detection
            • Evaluation Metrics
            • COCO JSON Format for Object Detection
            • YOLO Basics
            • YOLOv4: Run Pretrained YOLOv4 on COCO Dataset
            • YOLOv4: Train on Custom Dataset
            • Annotation Conversion: COCO JSON to YOLO Txt
            • YOLOv4: Training Tips
            • YOLOv5: Train Custom Dataset
            • Scaled YOLOv4
            • YOLOv3: Train on Custom Dataset
            • Histogram of Oriented Gradients (HOG)
            • Overview of Region-based Object Detectors
        • NLP
          • Text Processing
            • Regular Expressions
            • Minimum Edit Distance
            • Words and Text Normalization
          • Languages Modeling (N-Gram)
            • N Gram
            • Evaluating Language Models
            • Generalization and Zeros
            • Smoothing
            • Perplexity’s Relation to Entropy
            • Summary (TL;DR)
          • Sentiment Classification
            • Naive Bayes Classifiers
            • Train Naive Bayes Classifiers
            • Optimizing for Sentiment Analysis
            • Evaluation
          • Logistic Regression
            • Generative and Discriminative Classifiers
            • Sigmoid
            • Cross Entropy
            • Learning in Logistic Regression
            • Gradient Descent
            • Regularization
            • Multinomial Logistic Regression
            • Logistic Regression: Summry
            • Logistic Regression in NLP
          • POS Taggig
            • POS-Tagging
            • HMM POS-Tagging
          • Sequence Processing with Recurrent Networks
            • RNN Summary
            • LSTM Summary
            • BPTT
            • Resource
          • Information Extraction
            • Named-Entity Recognition
          • Lecture Notes
            • 00-Introduction
            • 01-WSD
            • 02-SA
            • 03-POS
            • 04-NER
            • 05-Parsing
            • 06-Summarization
            • 07-QA
            • 08-NLU
            • 10-DM
            • 09-NLG
            • 11-IR
            • 12-Vision
        • PyTorch
          • 🔥 Getting Started
            • Tensor
            • Autograd
            • Build & Train NN
            • PyTorch Modules and Classes
            • Learn PyTorch with Example
            • 👨‍🏫 Tutorial: Train a Classifier
            • 📈 Visualization with TensorBoard
            • 🤔 PyTorch Understanding
            • 📚 PyTorch Resources
          • 📖 DL with PyTorch
            • Pretrained Networks
            • PyTorch Tensor
            • Real-world Data Representation Using Tensors
            • The Mechanics of Learning
            • Using Neural Network to Fit Data
            • Learning from Images
            • Using Convolution to Generalize
          • 🧾 PyTorch Recipes
            • 🔥 Transfer Learning for Computer Vision
            • Saving and Loading Checkpoints
            • nn ModuleList vs. Sequential
            • 🔥 Custom Datasets and Transforms
            • 🔥🧾 General Training Steps Using PyTorch
            • Saving and Loading Models
            • Data Augmentation
            • TorchScript
            • Performance Measurement
          • 📈 Training
            • Use tmux
            • Running Jupyter Notebook/Lab on a remote server
            • Useful Tools for Training Neural Networks
            • Training Issues
          • 🔖 Config Manaegment
            • YACS
            • Hydra: Basics
            • Hydra: Advanced
          • ‼️ Issues & Gotchas
            • Model Registration
      • Coding
        • Python
          • Python Basics
            • Getting Started
            • args and kwargs
            • zip
            • Modules and Packages
            • Underscores
            • Terminal Input & Output
            • String
            • f-string
            • Sorting
            • Assertion
            • Function: First Class Object
            • Function: Lambda Function
            • Function: Return `None`
            • Looping and Iterations
            • Generator
            • Import
          • Python Advance
            • Decorator: Basics
            • Decorator: Advance
          • Data Structures and Collections
            • Dictionaries, Maps, and Hashtables
            • Array Data Structure
            • Records, Structs, and Data Transfer Objects
            • Sets
            • Stacks
            • Queues
            • Priority Queues
            • Dictionary Tricks
            • [Collections] Namedtuple
            • [Issues] List
            • [Issues] Dictionary
          • Files
            • Working with Files
            • File I/O
            • pathlib
            • glob
          • Serialization
            • JSON
            • YAML
          • OOP
            • OOP Basics
            • Operator Overloading
            • Object Comparison
            • String Conversion
            • Define Your Own Exception Classes
            • Object Cloning
            • Abstract Base Class (ABC)
            • Class vs Instance Variable
            • Instance, Class, and Static Methods
            • Property
          • Best Practice
            • Beautiful Python Code with PEP 8
            • Documenting Python Code
            • pre-commit
          • Testing
            • Getting Started
            • Pytest
          • Numpy
            • Numpy Getting Started
            • Stack and Concatenate
            • Numpy 1D Array
            • Numpy Tile
            • Numpy Random
          • Pandas
            • Pandas Getting Started
          • Visualization
            • Matplotlib Getting Started
            • Plotly
            • Matplotlib Issues
            • Visualization Cheatsheet
          • Ipython
            • IPython and Shell Commands
          • Concurrency
            • Concurrency 101
            • Thread and Thread Pool
            • ThreadPoolExecutor
          • Useful Packages
            • argparse
            • Logging
            • loguru
          • Issue & Solution
            • Magic Method
        • Docker
          • Getting Started
            • What is Docker?
            • Container
            • Image
            • Dockerfile
            • Dockerfile Best Practice
            • Docker Volume
          • Best Practices
          • Recipes
            • Use GPU within a Docker Container
        • Linux
          • Getting Started
            • Introduction to Linux
            • Information, Navigation, and Management Commands
            • Text Files, Networking, and Archiving Commands
            • Shell Scripting
            • Cheatsheet
          • Linux Recipes
          • Linux Commands
            • export
            • tee
            • chmod
        • Git
          • Git Operations
            • Git Squashing
          • Git Recipes
            • GitHub Profile
          • GitHub Actions
            • Getting Started
            • Actions Usage
            • Customization Techniques
            • Expressions
        • C++
      • CS
        • Algo
          • Algo Basics
            • Big O Notation
            • Binary Search
            • Recursion
          • Data Structure
            • Array and Linked List
            • Hash Table
          • Sort
            • Selection Sort
            • Quick Sort
            • Merge Sort
          • Graph
          • Leetcode
            • Linked List
        • Software Engineering
          • Design Patterns
            • SOLID Principles
          • High Quality Systems: Implementation
            • Clean Code
          • Best Practice
            • CI/CD
        • Project Management
          • Tutorials
            • Basics
          • Project Management Foundations
            • Embarking PM Career
            • Become Effective PM
            • PM Life Cycle Methodologies
            • Organization Structure & Culture
            • Glossary
          • Project Initiation
            • Project Initiation Fundamentals
            • Define Project Goals, Scope, and Success-criteria
            • Work Effectively With Stakeholders
            • Utilize Resources and Tools
            • Glossary
          • Project Planning
            • Begin Planning Phase
            • Build Project Plan
            • Manage Budget Procurement
            • Manage Risk
            • Organize Communication Documentation
          • Project Execution
            • Intro Project Execution
            • Quality Management And Continuous Improvement
            • Data-informed Decision Making
            • Leadership And Influencing Skills
            • Effective Project Communication
          • Agile Project Management
            • Agile Foundamentals
            • Scurm 101
            • Implement Scrum
            • Apply Agile
          • Capstone
            • Init Project
            • Build Project Plan
            • Maintain Quality
            • Effective Stakeholder Communication
        • Practical Skills
          • Shell
      • Notes
        • GIE
          • Vorlesung
            • 1. Vorlesung
            • 2. Vorlesung
            • 3. Vorlesung
            • 4. Vorlesung
            • 5. Vorlesung
            • 6. Vorlesung
            • 7. Vorlesung
            • 8. Vorlesung
            • 9. Vorlesung
            • 10. Vorlesung
            • 11. Vorlesung
        • Telematics
          • Lecture Notes
            • Glossary
            • Router
            • Internet Routing
            • Label Switching
            • Software Defined Networks (SDNs)
            • Network Function Virtualization (NFV)
            • Internet Congestion Control
            • Ethernet
            • Data Center
            • TCP Evolution
            • Access Networks
          • Understanding
            • OSI Model
            • Circuit Switching Vs. Packet Switching
            • MPLS
            • Control Plane Vs. Data Plane
            • TCP
            • Ethernet Basics
            • IP Address & Subnet
        • MMWAB
          • Lecture_notes
            • Einführung
            • Phänomene, Teilsysteme, Wirkungsbeziehungen
            • Die Sinne des Menschen
            • Wirkungskreis Mensch-Maschine-Mensch
            • Quantitative Modelle der Informationsverarbeitung
            • Hinweise für den Modellgestützten Systementwurf
            • Qualitative Gestaltungsregeln, Normen, Richtlinien
            • Klausur Vorbereiten
        • Thesis
          • Read Papers
            • How to Read Papers Efficiently?
            • Advice on Reading Research Papers (by Prof. Andrew Ng)
          • Write Papers
            • How to Write Papers Efficiently?
            • Tools for Writing Paper
            • Scientific Paper Structure
            • Abstract
            • Introduction
            • Methods
            • Results
            • Discussion
            • Improve Writing: Transitions
            • Improve Writing: Describe Trends
            • Improve Writing: Vocabulary
          • Presentation
        • Jobs
          • Resume
          • Cover Letter
            • Cover Letter Tutorial
            • Cover Letter Guideline
            • Cover Letter: Opening
            • Cover Letter: Closing
            • STAR Method
            • Common Phrases for Cover Letter
          • Anschreiben
            • Anschreiben Tutorials
            • Einleitung
            • Schulusssatz
            • Soft Skills
          • Salary
            • Gehalt Overview
            • Brutto und Netto
            • Gehaltsbestandteil
            • Weihnachtsgeld
            • Urlaubsgeld
            • Gehaltsverhandlung
          • Interview
            • Interview Confirmation
            • Job Interview Tutorial
            • Vorstellungsgespräch
            • Post-interview: Thank-You Email
            • Post Interview: Follow-up Email
          • Offer
            • Offer Acceptance
          • Career
            • How to Uncover Job Opportunities
        • SI
          • Math
            • Ereignis und Wahrscheinlichkeit
            • Delta-Distribution
            • Zufallsvariable
            • Zweidimensionale Zufallsvariable
            • Differenzierensregeln für Matrizen
            • HMM und Wonham Filter
            • Gaußverteilung
          • Wertdiskrete Systeme
            • Wert- und Zeitdiskrete Systeme
            • Zustandsschätzung
          • Wertekontinuierliche lineare Systeme
            • Statische und Dynamische Systeme
            • Zustandsschätzung: Kalman Filter
          • Wertekontinuierliche Nichtlineare Systeme
            • Statische und Dynamische Systeme
            • NLKF: Nichtlineare Schätzung
            • Berechnung der Momente (UKF)
            • Ensemble Kalmanfilter (EnKF)
          • Allgemeine Systeme
            • Motivation
            • Dirac’sche Deltafunktion
            • Funktionen von Zufallsvariablen
            • Probabilistische Systemmodelle
            • Abstraktion
            • Prädiktion nichtlinearer Systeme
            • Filterschritt für nichtlineare Systeme
            • Faktorgraphen und Message Passing
            • Vereinfachte Filterung
            • Einfache Filter für stark nichtlineare Systeme
            • Zusammenfassung
          • Sample-basierte Filter
            • Empirische Momente
            • Reapproximation von Dichten
            • Partikel Filter
            • Einschub: Gauß Rechenregeln
            • Progressive Filterung
          • Zusammenfassung
            • Mindmap
            • Allgemeine Fragen
            • Wertediskrete Systeme
            • Wertekontinuierliche lineare Systeme
            • Schwach nichtlineare wertekontinuierliche Systeme
            • Allgemeine Systeme
            • Sampling
            • Häufige Prüfungsfragen
          • Understanding
            • Kalman Filter
            • KF Family: LKF
            • KF Family: EKF
            • KF Family: ES-EKF
            • EKF Limitations
            • KF Family: UKF
    • Projects
    • Projects
      • Pandas
      • PyTorch
      • scikit-learn
    • Experience
    • Blog
      • SMART Goals
      • Homebrew
      • Documentation Page Fontmatter Template
    • Publications
      • Flying Guide Dog: Walkable Path Discovery for the Visually Impaired Utilizing Drones and Transformer-based Semantic Segmentation
      • An example preprint / working paper
      • An example journal article
      • An example conference paper
    • Recent & Upcoming Talks
      • Example Talk
    • Teaching
      • Learn JavaScript
      • Learn Python
    • AI
      • ML
        • ML Fundamentals
          • Math Basics
          • e2e ML Project
          • Evaluation
          • ML Algo overview
        • Model Selection
          • Objective Function
          • Bias Variance Tradeoff
          • Cross Validation
        • Regression
          • Linear Regression
          • Polynomial Regression
          • Kernelized Ridge Regression
        • Classification
          • K Nearest Neighbors
          • Logistic Regression: Basics
          • Logistic Regression: Probabilistic view
          • SVM: Basics
          • SVM: Kernel Methods
          • SVM: Kernelized SVM
        • Decision Trees
          • CART
        • Ensemble Learning
          • Why ensemble learning?
          • Voting Classifier
          • Random Forest
          • Ensemble Learners
          • Boosting
          • Bagging and Pasting
          • AdaBoost
        • Non-parametric
          • Linear Discriminant Functions
          • LDA
        • Unsupervised Learning
          • Gaussian Mixture Model
          • PCA
      • DL
        • NN Basics
          • Perceptron
          • 👍 Activation Functions
          • 👍 Loss Functions
          • MLP and Backprop
          • Math: Softmax
          • Generalization
          • Generalization: Dropout
          • 👍 Generalization: Data Augmentation
        • Efficient Training
          • Optimizers
          • 👍 Batch Normalization
        • Unsupervised Learning
          • Auto Encoder
          • Hopfield Nets
          • Bolzmann Machine
          • Restricted Boltzmann Machines
        • CNN
          • TDNN
          • 👍 CNN Basics
          • 👍 CNN Intuition and Visualization
          • CNN History
          • Computer Vision
          • Resources
        • Parallelism
          • Parallelism and Vectorization
        • RNN
          • Recurrent Neural Networks
          • LSTM
          • 👍 RNN Summary
          • 👍 LSTM Summary
          • BPTT
          • RNN Implementation
          • RNN Resource
        • Encoder-Decoder
          • Seq2Seq
          • 👍 Attention
          • 👍 Transformer
      • CV
        • CV Lecture
          • Pattern Recognition
          • Face Detection: Color-Based
          • Face Detection: Neural-Network-Based
          • Face Recognition: Traditional Approaches
          • Face Recognition: Features
          • Face Recognition: Deep Learning
          • Facial Feature Detection
          • Facial Expression Recognition
          • People Detection: Global Approaches
          • People Detection: Part-based Approaches
          • People Detection: Deep Learning Approaches
          • Tracking
          • Tracking 2
          • Body Pose
          • Gesture Recognition
          • Action & Activity Recognition
          • Action & Activity Recognition 2
        • Segmentation
          • Semantic Segmentation Overview
          • Semantic Segmentation with PyTorch
        • Face
          • Modern Face Recognition Overview
          • Eigenface
        • Visual Transformer
          • Attention Mechanism
          • Transformer
          • Visual Transformer
        • HPE
          • HPE Datasets
        • Object Detection
          • Evaluation Metrics
          • COCO JSON Format for Object Detection
          • YOLO Basics
          • YOLOv4: Run Pretrained YOLOv4 on COCO Dataset
          • YOLOv4: Train on Custom Dataset
          • Annotation Conversion: COCO JSON to YOLO Txt
          • YOLOv4: Training Tips
          • YOLOv5: Train Custom Dataset
          • Scaled YOLOv4
          • YOLOv3: Train on Custom Dataset
          • Histogram of Oriented Gradients (HOG)
          • Overview of Region-based Object Detectors
      • NLP
        • Text Processing
          • Regular Expressions
          • Minimum Edit Distance
          • Words and Text Normalization
        • Languages Modeling (N-Gram)
          • N Gram
          • Evaluating Language Models
          • Generalization and Zeros
          • Smoothing
          • Perplexity’s Relation to Entropy
          • Summary (TL;DR)
        • Sentiment Classification
          • Naive Bayes Classifiers
          • Train Naive Bayes Classifiers
          • Optimizing for Sentiment Analysis
          • Evaluation
        • Logistic Regression
          • Generative and Discriminative Classifiers
          • Sigmoid
          • Cross Entropy
          • Learning in Logistic Regression
          • Gradient Descent
          • Regularization
          • Multinomial Logistic Regression
          • Logistic Regression: Summry
          • Logistic Regression in NLP
        • POS Taggig
          • POS-Tagging
          • HMM POS-Tagging
        • Sequence Processing with Recurrent Networks
          • RNN Summary
          • LSTM Summary
          • BPTT
          • Resource
        • Information Extraction
          • Named-Entity Recognition
        • Lecture Notes
          • 00-Introduction
          • 01-WSD
          • 02-SA
          • 03-POS
          • 04-NER
          • 05-Parsing
          • 06-Summarization
          • 07-QA
          • 08-NLU
          • 10-DM
          • 09-NLG
          • 11-IR
          • 12-Vision
      • PyTorch
        • 🔥 Getting Started
          • Tensor
          • Autograd
          • Build & Train NN
          • PyTorch Modules and Classes
          • Learn PyTorch with Example
          • 👨‍🏫 Tutorial: Train a Classifier
          • 📈 Visualization with TensorBoard
          • 🤔 PyTorch Understanding
          • 📚 PyTorch Resources
        • 📖 DL with PyTorch
          • Pretrained Networks
          • PyTorch Tensor
          • Real-world Data Representation Using Tensors
          • The Mechanics of Learning
          • Using Neural Network to Fit Data
          • Learning from Images
          • Using Convolution to Generalize
        • 🧾 PyTorch Recipes
          • 🔥 Transfer Learning for Computer Vision
          • Saving and Loading Checkpoints
          • nn ModuleList vs. Sequential
          • 🔥 Custom Datasets and Transforms
          • 🔥🧾 General Training Steps Using PyTorch
          • Saving and Loading Models
          • Data Augmentation
          • TorchScript
          • Performance Measurement
        • 📈 Training
          • Use tmux
          • Running Jupyter Notebook/Lab on a remote server
          • Useful Tools for Training Neural Networks
          • Training Issues
        • 🔖 Config Manaegment
          • YACS
          • Hydra: Basics
          • Hydra: Advanced
        • ‼️ Issues & Gotchas
          • Model Registration
    • Coding
      • Python
        • Python Basics
          • Getting Started
          • args and kwargs
          • zip
          • Modules and Packages
          • Underscores
          • Terminal Input & Output
          • String
          • f-string
          • Sorting
          • Assertion
          • Function: First Class Object
          • Function: Lambda Function
          • Function: Return `None`
          • Looping and Iterations
          • Generator
          • Import
        • Python Advance
          • Decorator: Basics
          • Decorator: Advance
        • Data Structures and Collections
          • Dictionaries, Maps, and Hashtables
          • Array Data Structure
          • Records, Structs, and Data Transfer Objects
          • Sets
          • Stacks
          • Queues
          • Priority Queues
          • Dictionary Tricks
          • [Collections] Namedtuple
          • [Issues] List
          • [Issues] Dictionary
        • Files
          • Working with Files
          • File I/O
          • pathlib
          • glob
        • Serialization
          • JSON
          • YAML
        • OOP
          • OOP Basics
          • Operator Overloading
          • Object Comparison
          • String Conversion
          • Define Your Own Exception Classes
          • Object Cloning
          • Abstract Base Class (ABC)
          • Class vs Instance Variable
          • Instance, Class, and Static Methods
          • Property
        • Best Practice
          • Beautiful Python Code with PEP 8
          • Documenting Python Code
          • pre-commit
        • Testing
          • Getting Started
          • Pytest
        • Numpy
          • Numpy Getting Started
          • Stack and Concatenate
          • Numpy 1D Array
          • Numpy Tile
          • Numpy Random
        • Pandas
          • Pandas Getting Started
        • Visualization
          • Matplotlib Getting Started
          • Plotly
          • Matplotlib Issues
          • Visualization Cheatsheet
        • Ipython
          • IPython and Shell Commands
        • Concurrency
          • Concurrency 101
          • Thread and Thread Pool
          • ThreadPoolExecutor
        • Useful Packages
          • argparse
          • Logging
          • loguru
        • Issue & Solution
          • Magic Method
      • Docker
        • Getting Started
          • What is Docker?
          • Container
          • Image
          • Dockerfile
          • Dockerfile Best Practice
          • Docker Volume
        • Best Practices
        • Recipes
          • Use GPU within a Docker Container
      • Linux
        • Getting Started
          • Introduction to Linux
          • Information, Navigation, and Management Commands
          • Text Files, Networking, and Archiving Commands
          • Shell Scripting
          • Cheatsheet
        • Linux Recipes
        • Linux Commands
          • export
          • tee
          • chmod
      • Git
        • Git Operations
          • Git Squashing
        • Git Recipes
          • GitHub Profile
        • GitHub Actions
          • Getting Started
          • Actions Usage
          • Customization Techniques
          • Expressions
      • C++
    • CS
      • Algo
        • Algo Basics
          • Big O Notation
          • Binary Search
          • Recursion
        • Data Structure
          • Array and Linked List
          • Hash Table
        • Sort
          • Selection Sort
          • Quick Sort
          • Merge Sort
        • Graph
        • Leetcode
          • Linked List
      • Software Engineering
        • Design Patterns
          • SOLID Principles
        • High Quality Systems: Implementation
          • Clean Code
        • Best Practice
          • CI/CD
      • Project Management
        • Tutorials
          • Basics
        • Project Management Foundations
          • Embarking PM Career
          • Become Effective PM
          • PM Life Cycle Methodologies
          • Organization Structure & Culture
          • Glossary
        • Project Initiation
          • Project Initiation Fundamentals
          • Define Project Goals, Scope, and Success-criteria
          • Work Effectively With Stakeholders
          • Utilize Resources and Tools
          • Glossary
        • Project Planning
          • Begin Planning Phase
          • Build Project Plan
          • Manage Budget Procurement
          • Manage Risk
          • Organize Communication Documentation
        • Project Execution
          • Intro Project Execution
          • Quality Management And Continuous Improvement
          • Data-informed Decision Making
          • Leadership And Influencing Skills
          • Effective Project Communication
        • Agile Project Management
          • Agile Foundamentals
          • Scurm 101
          • Implement Scrum
          • Apply Agile
        • Capstone
          • Init Project
          • Build Project Plan
          • Maintain Quality
          • Effective Stakeholder Communication
      • Practical Skills
        • Shell
    • Notes
      • GIE
        • Vorlesung
          • 1. Vorlesung
          • 2. Vorlesung
          • 3. Vorlesung
          • 4. Vorlesung
          • 5. Vorlesung
          • 6. Vorlesung
          • 7. Vorlesung
          • 8. Vorlesung
          • 9. Vorlesung
          • 10. Vorlesung
          • 11. Vorlesung
      • Telematics
        • Lecture Notes
          • Glossary
          • Router
          • Internet Routing
          • Label Switching
          • Software Defined Networks (SDNs)
          • Network Function Virtualization (NFV)
          • Internet Congestion Control
          • Ethernet
          • Data Center
          • TCP Evolution
          • Access Networks
        • Understanding
          • OSI Model
          • Circuit Switching Vs. Packet Switching
          • MPLS
          • Control Plane Vs. Data Plane
          • TCP
          • Ethernet Basics
          • IP Address & Subnet
      • MMWAB
        • Lecture_notes
          • Einführung
          • Phänomene, Teilsysteme, Wirkungsbeziehungen
          • Die Sinne des Menschen
          • Wirkungskreis Mensch-Maschine-Mensch
          • Quantitative Modelle der Informationsverarbeitung
          • Hinweise für den Modellgestützten Systementwurf
          • Qualitative Gestaltungsregeln, Normen, Richtlinien
          • Klausur Vorbereiten
      • Thesis
        • Read Papers
          • How to Read Papers Efficiently?
          • Advice on Reading Research Papers (by Prof. Andrew Ng)
        • Write Papers
          • How to Write Papers Efficiently?
          • Tools for Writing Paper
          • Scientific Paper Structure
          • Abstract
          • Introduction
          • Methods
          • Results
          • Discussion
          • Improve Writing: Transitions
          • Improve Writing: Describe Trends
          • Improve Writing: Vocabulary
        • Presentation
      • Jobs
        • Resume
        • Cover Letter
          • Cover Letter Tutorial
          • Cover Letter Guideline
          • Cover Letter: Opening
          • Cover Letter: Closing
          • STAR Method
          • Common Phrases for Cover Letter
        • Anschreiben
          • Anschreiben Tutorials
          • Einleitung
          • Schulusssatz
          • Soft Skills
        • Salary
          • Gehalt Overview
          • Brutto und Netto
          • Gehaltsbestandteil
          • Weihnachtsgeld
          • Urlaubsgeld
          • Gehaltsverhandlung
        • Interview
          • Interview Confirmation
          • Job Interview Tutorial
          • Vorstellungsgespräch
          • Post-interview: Thank-You Email
          • Post Interview: Follow-up Email
        • Offer
          • Offer Acceptance
        • Career
          • How to Uncover Job Opportunities
      • SI
        • Math
          • Ereignis und Wahrscheinlichkeit
          • Delta-Distribution
          • Zufallsvariable
          • Zweidimensionale Zufallsvariable
          • Differenzierensregeln für Matrizen
          • HMM und Wonham Filter
          • Gaußverteilung
        • Wertdiskrete Systeme
          • Wert- und Zeitdiskrete Systeme
          • Zustandsschätzung
        • Wertekontinuierliche lineare Systeme
          • Statische und Dynamische Systeme
          • Zustandsschätzung: Kalman Filter
        • Wertekontinuierliche Nichtlineare Systeme
          • Statische und Dynamische Systeme
          • NLKF: Nichtlineare Schätzung
          • Berechnung der Momente (UKF)
          • Ensemble Kalmanfilter (EnKF)
        • Allgemeine Systeme
          • Motivation
          • Dirac’sche Deltafunktion
          • Funktionen von Zufallsvariablen
          • Probabilistische Systemmodelle
          • Abstraktion
          • Prädiktion nichtlinearer Systeme
          • Filterschritt für nichtlineare Systeme
          • Faktorgraphen und Message Passing
          • Vereinfachte Filterung
          • Einfache Filter für stark nichtlineare Systeme
          • Zusammenfassung
        • Sample-basierte Filter
          • Empirische Momente
          • Reapproximation von Dichten
          • Partikel Filter
          • Einschub: Gauß Rechenregeln
          • Progressive Filterung
        • Zusammenfassung
          • Mindmap
          • Allgemeine Fragen
          • Wertediskrete Systeme
          • Wertekontinuierliche lineare Systeme
          • Schwach nichtlineare wertekontinuierliche Systeme
          • Allgemeine Systeme
          • Sampling
          • Häufige Prüfungsfragen
        • Understanding
          • Kalman Filter
          • KF Family: LKF
          • KF Family: EKF
          • KF Family: ES-EKF
          • EKF Limitations
          • KF Family: UKF

    On this page

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      AI
      ML
      Ensemble Learning
      Boosting

      Boosting

      Boosting

      Refers to any Ensemble method that can combine serval weak learners into a strong learner

      💡 General idea: train predictors sequentially, each trying to correct its predecessor.

      Popular boosting methods:

      • AdaBoost
      • Gradient Boost
      Last updated on 2024-09-05
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      Bagging and Pasting 2020-11-07 →

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