Haobin Tan
  • 🗒 Posts
  • 🤖 AI
    • Machine Learning
    • Deep Learning
    • Computer Vision
    • Natural Language Processing
    • PyTorch
  • 🧑‍💻 Coding
    • Python
    • Docker
    • Linux
    • Git
    • C++
  • 💻 CS
    • Algorithm
    • Software Engineering
    • Project Management
    • Practical Skills
  • 🧾 Résumé
  • Docs
    • 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

PyTorch

2023-10-26 · 1 min read
Go to Project Site

PyTorch is a Python package that provides tensor computation (like NumPy) with strong GPU acceleration.

Last updated on 2024-08-27
Hugo Wowchemy Markdown
Haobin Tan
Authors
Haobin Tan
Software Concept Developer / Innovation Manager for Algorithms
← Pandas 2023-10-26
scikit-learn 2023-10-26 →

© 2024 Haobin Tan. This work is licensed under CC BY NC ND 4.0

Published with Hugo Blox Builder — the free, open source website builder that empowers creators.