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
            • Why emsemble 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

  • Why emsemble learners?
Docs
AI
ML
Ensemble Learning
Ensemble Learners

Ensemble Learners

Why emsemble learners?

Lower error

  • Each learner (model) has its own bias. It we put them together, the bias tend to be reduced (they fight against each other in some sort of way)
  • Less overfitting
  • Tastes great
Last updated on 2024-09-05
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