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
          • Glossary
        • 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
    • Finance
      • Personal Finance
        • Intro to Personal Finance
          • Approaching Your Finance with Purpose
          • Understanding Net Worth and Credit Score
          • Assessing Cash Flow and Taxes
          • Planning and Budgeting for Future
      • ETF
        • ETF 101
        • Theory
          • Passive Investment
          • What Is Index
          • What Are ETFs
          • ETF Advantages
          • ETF Disadvantages
        • Risk
          • ETF Risk
          • Reduce Risk
          • Create Risk Profile
        • Strategy and Portfolio
          • World Portfolio
          • World Index Overview
          • Regional Weighting
          • ETF Portfolios
          • 70/30 Portfolio
        • ETF Selection
          • Find the Right ETF
          • Fund Volume
          • Costs and Fees
          • Distributing / Accumulating ETFs?
          • Replication Method
          • Tracking Difference
          • Currency Risk
          • Fund Domicile
          • Factsheet
        • ETF Trading
          • Brokerage Account
          • Lump-Sum ETF Investment
          • Saving Plan
          • Tax
          • Reblancing
          • Decumulating
    • 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
  • Experience
  • Blog
    • 云南
    • 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
  • 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
        • Glossary
      • 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
  • Finance
    • Personal Finance
      • Intro to Personal Finance
        • Approaching Your Finance with Purpose
        • Understanding Net Worth and Credit Score
        • Assessing Cash Flow and Taxes
        • Planning and Budgeting for Future
    • ETF
      • ETF 101
      • Theory
        • Passive Investment
        • What Is Index
        • What Are ETFs
        • ETF Advantages
        • ETF Disadvantages
      • Risk
        • ETF Risk
        • Reduce Risk
        • Create Risk Profile
      • Strategy and Portfolio
        • World Portfolio
        • World Index Overview
        • Regional Weighting
        • ETF Portfolios
        • 70/30 Portfolio
      • ETF Selection
        • Find the Right ETF
        • Fund Volume
        • Costs and Fees
        • Distributing / Accumulating ETFs?
        • Replication Method
        • Tracking Difference
        • Currency Risk
        • Fund Domicile
        • Factsheet
      • ETF Trading
        • Brokerage Account
        • Lump-Sum ETF Investment
        • Saving Plan
        • Tax
        • Reblancing
        • Decumulating
  • 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
Docs
AI
PyTorch
🧾 PyTorch Recipes
🔥🧾 General Training Steps Using PyTorch

🔥🧾 General Training Steps Using PyTorch

Open in Google Colab

General steps:

  1. Set device

  2. Set Dataset and DataLoader

  3. Define network model

  4. Build network model

  5. Define loss function and optimizer

  6. Define training process

  7. Train the model

  8. Store/Load weights

Last updated on 2024-09-05

← 🔥 Custom Datasets and Transforms 2020-11-26
Saving and Loading Models 2021-01-17 →

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