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Lê Phong Phú
  • About Me!
  • AI Expert Roadmap
    • PyTorch
      • PyTorch Fundamentals
        • 1. Introduction to PyTorch
        • 2. Introduction to Computer Vision with PyTorch
        • 3. Introduction to Natural Language Processing with PyTorch
        • 4. Introduction to Audio Classification with PyTorch
      • Intermediate DL with Pytorch
        • 1_TrainingRobustNN
        • 2_Image&CNN
        • 3_Sequences&RNN
        • 4_Multi-Input&Multi-Output
    • Machine Learning
      • 01_ML_General
      • 02_ML_Supervised Learning
      • 03_ML_Unsupervised Learning
    • Mamba
      • 00_Sequence Modelling, S4 and Mamba
    • Transformers (CV&NLP)
      • NLNet
      • 01_Pure Transformer
        • ViT
        • Segformer
      • 02_Hybrid Transformer
        • DETR
        • Deformable DETR
        • DINO (Detection)
      • 99_Unfilter
        • LG-Transformer
        • Image GPT
        • Points as Queries
        • VST
        • MAXViT
        • ViTMAE-Detect
        • MAGNETO
        • AIT
        • MTV
        • PiT
        • Swin
        • PVTv2
        • PVT
        • FAVOR+
        • T2T-ViT
        • CaiT
        • CCT
        • DeiT
        • SSA
        • SA3D
    • [NLP] Natural Language Processing
      • 01_[LLMs] Large Language Models
      • [MoEs] Mixture of Experts
      • LLM Techniques
      • Attention is All You Need
      • Positional Encoding
      • Tokenization
      • MICLe
    • [CV] Computer Vision
      • MLP-based Classification
        • MLP-Mixer
        • FNet
        • EANet
      • 01_[SL] Supervised Learning
        • 01_Classification
          • Convolution Variants
          • 1x1 Convolution
          • EfficientNetV2
          • ConvNeXtV2
        • 02_Detection
          • ConvMixer
          • SOLO
          • YOLOX
          • YOLOR
          • AugFPN
          • BoT_Cls
          • BoF_OD
          • YOLOv3
          • YOLOv4
          • YOLOv5
          • YOLOv6
          • YOLOv7
          • YOLOv8
          • YOLOv9
          • YOLO-NAS
          • TPH-YOLOv5
          • TPH-YOLOv5++
          • ViTDET
        • 03_Segmentation
          • Object Instance Survey 2022
          • 01_Instance Segmentation
          • 02_Semantic Segmentation
          • 03_Panoptic Segmentation
          • 04_3D Segmentation
          • 05_Unsupervised Segmentation
          • BMask RCNN
          • ISTR
          • Transfuse
        • 04_[IS] Interactive Segmentation
          • Interactive Segmentation Techniques
          • 02_3D Interactive Segmentation
          • 03_Video Object Segmentation
          • SAM
          • HA_SAM
          • CFR-ICL
          • MST
          • ECONet
          • SimpleClick
          • FocusCut
          • f-BRS
          • iSegformer
        • 05_Object Tracking
          • 00_ObjectTracking
          • Sort
          • DeepSort
          • FairMOT
          • ByteTrack
          • StrongSORT
          • Tracktor
          • JDE
          • CenterTrack
          • PermaTrack
          • TransTrack
          • TrackFormer
          • BoT-SORT
        • 06_Face Recognition
        • 07_Image Stitching
        • 08_Image Restoration
        • 06_Refinement
          • BPR
        • 10_Scene Understanding
          • CPNet
        • 11_Human Pose Estimation
          • 3D Human Pose
          • Human Pose
        • 12_[SR] Super Resolution
          • Bicubic++
        • 13_VideoPropagation
        • 14_Image Mating
        • 15_Knowledge Distillation
        • 16_Others
      • 02_[UL] Unsupervised Learning
        • 00_Unsupervised Learning
        • 02_Deep Clustering
          • 00_K_Clusters Decision
          • Deep Cluster
          • Cluster Fit
          • DEC
          • Improving Relational Regularized Autoencoders with Spherical Sliced Fused G
          • Taxanomy
          • DeepDPM
          • BCL
          • VaDE
          • t-SNE
          • Tree-SNE
        • 04_Diffusional Models
      • 03_[SSL] Self-Supervised Learning
        • 00_Self-Supervised Learning
        • 01_Contrastive Learning
          • CPC
          • DIM
          • CMC
          • AMDIM
          • SimCLR
          • MoCo
          • MoCov2
          • YADIM
          • VICReg
          • CSL
          • Towards Domain-Agnostic Contrastive Learning
          • Non-Parametric Instance Discrimination
          • Video Contrastive Learning with Global Context
          • SupCon
          • Barlow Twin
        • 02_Predictive Tasks
        • 03_Bootstrapping
          • BYOL
        • 04_Regularization
        • 05_Masked Image Models
          • Patch Localization
          • MAE
          • SimMIM
          • DINO
        • 06_Pretext Tasks
          • PIRL
        • 07_Clustering-based
          • SwAV
      • 04_Semi-Supervised Learning
        • Fully-/Semi-/Weakly-/ Learning
        • 01_Self-training
          • Pseudo-label
          • Noisy Student
        • 02_Consistency Regularization
          • Temporal Ensembling
          • Mean Teacher
          • VAT
          • UDA
        • 03_Hybrid Methods
          • MixUp
          • MixMatch
          • ReMixMatch
          • FixMatch
          • FixMatch (unmerge)
      • 05_Multi-learning Paradigm
        • 00_Multi-learning
        • 01_Multitask
        • Gradient Surgery
        • EtE Multi-task Learning with Attention
        • MTL for Dense Predictions
        • MTL using Uncertainty
        • Which Task learned together
        • GradNorm
        • OM-Net
        • 06_Multi-task Learning
      • 06_Generative Models
        • 00_Generative Models
        • 01_Autoencoders
          • AE vs Others
          • Sparse AE
          • Denoising AE
          • Contractive AE
          • Variational AE
          • DELG
        • 02_GAN
      • Graph Convolutional Networks
        • 00_Graph Convolutional Networks
      • Neural Radiance Fields (NeRFs)
      • Deep Belief Networks
    • Multimodal Models
    • Bag of Freebies - BOF
      • 01_Augmentation
        • Mosaic
        • Cut Out
        • Mix Up
      • 02_Loss Functions
        • 01_Classification Loss
        • 02_Segmentation Loss
        • 03_Object Detection Loss
        • 04_Self-Supervised Loss
        • 05_Interactive Segmentation Loss
      • 03_Optimizer
      • 04_Normalization
        • 00_Normalization
      • 05_Regularization
      • 06_Label Assignment
        • 00_Label Assignment
        • OTA
        • SimOTA
      • 07_Auxiliary Head
    • Bag of Specials - BoS
      • Feature Pyramid
        • RCNet
      • Receptive Field
      • Attention
        • 00_Attention Modules
        • SENet
        • CBAM
        • DANet
        • SDANet
        • AttaNet
        • HaloNets
        • GCNet
        • DeepSquare
        • LBAM
        • External-Attention
        • PCT
        • Residual Attention
        • DCANet
        • GANet
        • Triplet Attention
        • Lambda Networks
        • ACTION
        • VAN
        • SegNeXt
      • Local-/Global- Features
        • Unifying Nonlocal Blocks for Neural Networks
        • Local Features
        • Global Features
      • Activation Functions
        • SiLU dSiLU
      • Post-Processing
        • Soft-NMS
        • NMW
        • WBF
      • Sliding Window
      • Graph Networks
      • Feature Fusion/Integration
      • Data-Centric
    • Others
      • Selected Top-Conference Papers
        • AAAI2021_Papers
        • CVPR2021_Papers
        • ECCV2020_Papers
        • ICCV2021_Papers
        • ICLM2022_Papers
      • Cheat Sheets
        • Pandas
      • Conference Schedule
  • Data Science
    • 03_DS_Discrete Distribution
    • Data Scientist Professional
      • 3. Statistical Experimentation Theory
      • 4. Statistical Experimentation in Python
      • 5. Model development in Python
      • 7. Data Management in SQL
    • Data...
    • ETL
    • Airflow
  • Cloud Computing
    • Azure Data Fundamental
    • Amazon Web Services
      • AWS - Cloud 101
      • AWS - Machine Learning Foundation (Lab)
        • 1. Introduction to MLF
        • 2. AI and ML
        • 3. ML Pipeline
        • 4. ML Tools and Services
        • 5. Wrapping it Up
      • AWS - Cloud Practitioner Essentials
      • AWS - GenAI
    • Google Cloud
    • IBM Watson
  • Big Data
    • PySpark
      • Introduction to PySpark
        • 1. Getting to know PySpark
        • 2. Manipulating Data
        • 3. Getting Started with ML Pipelines
        • 4. Model Tuning and Selection
      • Big Data Fundamentals with PySpark
        • 1. Introduction to BigData Analysis with Spark
        • 2. Programming in PySpark RDD’s
        • 3. PySpark SQL & DataFrames
        • 4. Machine Learning with PySpark MLlib
  • English
    • Reading
    • Listening
    • Speaking
      • Speaking_Part1
        • 1_Speaking Part 1
        • 2_Speaking Part 1
        • 3_Speaking Part 1
        • 4_Speaking Part 1
        • 5_Speaking Part 1
        • 6_Speaking Part 1
        • 7_Speaking Part 1
        • 8_Speaking Part 1
        • 9_Speaking Part 1
        • 10_Speaking Part 1
        • 11_Speaking Part 1
        • 12_Speaking Part 1
        • 13_Speaking Part 1
        • 14_Speaking Part 1
        • 15_Speaking Part 1
        • 16_Speaking Part 1
        • 17_Speaking Part 1
        • 18_Speaking Part 1
        • 19_Speaking Part 1
        • 20_Speaking Part 1
        • 21_Speaking Part 1
        • 22_Speaking Part 1
        • 23_Speaking Part 1
      • Speaking_Part2
        • 1_Speaking Part 2
        • 2_Speaking Part 2
        • 3_Speaking Part 2
        • 4_Speaking Part 2
        • 5_Speaking Part 2
        • 6_Speaking Part 2
        • 7_Speaking Part 2
        • 8_Speaking Part 2
        • 9_Speaking Part 2
        • 10_Speaking Part 2
        • 11_Speaking Part 2
        • 12_Speaking Part 2
        • 13_Speaking Part 2
        • 14_Speaking Part 2
        • 15_Speaking Part 2
        • 16_Speaking Part 2
        • 17_Speaking Part 2
        • 18_Speaking Part 2
        • 19_Speaking Part 2
        • 20_Speaking Part 2
        • People
        • Places
          • Visited House
        • Events
        • Activities
          • Interesting Job
        • Things
      • Speaking_Part3
        • Advertisements
        • Outdoor Activities
        • Navigation and Exploration
        • Fast Food
        • Air Pollution
        • Free Time
        • Interesting Movie
        • Gifts
        • Independence in Children
        • Noisy
        • Complain
        • T-shirts
        • Value of Money
        • Restaurant
        • Global
        • Relaxation
        • Special Places
      • Mixed-Test
        • 01_Mix_Language
    • Writing
      • Writing_Task1
        • Paraphrase
        • Overview Sentence
        • Grammar
        • Charts
          • Line - Average Montly Temperatures
          • Line - Fuels
          • Line - Birth Rate
          • Line - River Water
          • Line - U.S Energy
          • Line - Areas of Crime
          • Line - Renewable Energy
          • Line - Oversea Visitors
          • Chart - People ate in the UK
          • Chart - Music Event Attendance
          • Chart - Wind Energy
          • Chart - Children Attend Sports in Australia
          • Chart - Weekly Hours in Australia
          • Chart - Films released vs Tickets sold
          • Chart - Average Retirement Age
        • Process
        • Maps
          • Library Ground
        • Table
        • Multiple Graphs
          • Life Expectancy
      • Writing_Task2
        • Opinion Essay
          • Higher Salary
          • Goal of Schools
          • Local History
          • Retirement Age
          • Happy Society
          • Food Necessary
          • Pay for more Art
          • Eradicate Poverty
          • Team Activities
          • Wild Animals and Birds
        • Discussion Essay
          • Sports
          • Make Money
          • Crime punished
          • Equipment for Student
          • Keep a Gun
        • Advantages and Disadvantages Essay
          • Live Away
          • Transform to Farms
        • Problem-Solution Essay
          • Extreme Sports
          • Spend Time Away From Families
      • Complex Sentence
      • If, Wish, Hope
    • Synonym Common Mistakes
    • Phrasal Verbs
    • TOEIC 990
  • Interview
    • Deep Learning Questions
      • C1_Mathematical Foundation
      • C2_Fundamentals of ML
      • C3_Fundamentals of DL
      • C4_Classic Network
      • C5_CNN
      • C6_RNN
      • C7_Target Detection
      • C8_Image Segmentation
      • C9_Reinforcement Learning
      • C10_Migration Learning
      • C13_Optimization Algorithm
      • C14_Super Parameter Adjustment
      • C15_Hetorogeneous Computing
    • Data Science Questions
  • Courses (Uni and Mooc)
    • AI Open Courses
    • DS Certificates
    • IBM Gen AI Engineering Professional Certificate
      • 10. Generative AI and LLMs: Architecture and Data Preparation
      • 11. Gen AI Foundational Models for NLP & Language Understanding
      • 12. Gen AI Language Modeling with Transformers
        • Module 1 - Fundamental Concepts of Transformer Architecture
        • Module 2 - Advanced Concepts of Transformer Architecture
      • 13. Generative AI Engineering and Fine-Tuning Transformers
      • 14. Generative AI Advanced Fine-Tuning for LLMs
      • 15. Fundamentals of AI Agents using RAG and Langchain
        • Module 1 - RAG Framework
        • Module 2 - Prompt Engineering and LangChain
      • 16. Project: Generative AI Applications with RAG and LangChain
    • Data Science Foundations: Data Structures and Algorithms Specialization
    • Flask - AI Applications
      • 1. Packaging Concepts
      • 2. Web App Deployment
      • 3. Creating AI Application
        • Sentiment Analysis
        • Emotion Detector
      • Deploy Deep Learning Models using Flask
    • Docker, Kubernetes & OpenShift
      • 1. Containers and Containerization
      • 2. Kubernetes Basics
      • 3. Managing Applications with Kubernetes
      • 4. The Kubernetes Ecosystem
      • 5. Final Assignments
    • Data Structures
      • 1. Introduction to DS&A
    • Algorithms
      • QE - Algorithms
      • Sorting Algorithms
        • Binary Search
        • Insertion Sort
        • Merge Sort
        • Quick sort
        • Heap sort
      • Divide and Conquer
      • Greedy Algorithm
      • Dynamic Programming
    • Operating System
      • QE - Operating System
      • 00_Operating System
    • CS231n Deep Learning for Computer Vision
      • 13. Self-Supervised Learning
    • CS480 Introduction to Machine Learning
      • 19. Attention and Transformer Networks
    • CS330 Multi-task and Meta Learning
      • 1. What is Multi-task Learning
    • Processing the Environment
      • Attention
    • Open VINO
    • Metaverse
      • 00_Metaverse
      • Spark AR
  • Research Projects
    • PPE Detection
      • Few-shot Data Sampling
    • Multiple Object Tracking
      • In-place Augmentation
    • Deep Clustering
      • Metrics
    • Defect Detection
      • 01_Defect_Improvement
      • Dataset: MVTec
      • Mixed supervision for surface-defect detection:
      • Practical Defect Detection
      • (Survey) Fabric Defect Detection
      • (Summary) Fabric Defect Detection
    • Medical Images
      • 01_Lung_Improvement
      • SANet
      • AnaXNet
      • 3D_EtoE Lung Cancer Screening
      • Semantics-enriched Representation
      • Attend And Compare
      • Recent Works
      • Kaggle_Medical Images
  • AI Engineer
  • Financial Invesment
    • 01_TPTrading
    • 02_BCTC
    • 03_Demand Side Platform (DSP)
    • 04_Business Models
    • Trading
      • 01_Technical Analysis
      • 02_Mentality
      • 03_Support and Resistance
  • Books
    • AI Books
    • Books
      • Persuasion IQ
      • Communication Skills
      • 48 Hours a Day
      • Maslow's Pyramid
      • MBTI
      • Tư Duy Ngược
    • Audio Books
  • Project Management
    • PM Methods
      • Agile
      • Scrum
      • Kanban
    • Foundations of PM
      • Module 1
      • Module 2
      • Module 3
      • Module 4
    • Project Initiation: Starting a Successul Projet
      • Module 1
      • Module 2
      • Module 3
      • Module 4
    • Project Planning: Putting It All Together
      • Module 1
    • Project Execution: Running the Project
    • Agile Project Management
    • Capstone: Applying Project Management in the Real World
  • Administrator
Lê Phong Phú
  • About Me!
  • AI Expert Roadmap
    • PyTorch
      • PyTorch Fundamentals
        • 1. Introduction to PyTorch
        • 2. Introduction to Computer Vision with PyTorch
        • 3. Introduction to Natural Language Processing with PyTorch
        • 4. Introduction to Audio Classification with PyTorch
      • Intermediate DL with Pytorch
        • 1_TrainingRobustNN
        • 2_Image&CNN
        • 3_Sequences&RNN
        • 4_Multi-Input&Multi-Output
    • Machine Learning
      • 01_ML_General
      • 02_ML_Supervised Learning
      • 03_ML_Unsupervised Learning
    • Mamba
      • 00_Sequence Modelling, S4 and Mamba
    • Transformers (CV&NLP)
      • NLNet
      • 01_Pure Transformer
        • ViT
        • Segformer
      • 02_Hybrid Transformer
        • DETR
        • Deformable DETR
        • DINO (Detection)
      • 99_Unfilter
        • LG-Transformer
        • Image GPT
        • Points as Queries
        • VST
        • MAXViT
        • ViTMAE-Detect
        • MAGNETO
        • AIT
        • MTV
        • PiT
        • Swin
        • PVTv2
        • PVT
        • FAVOR+
        • T2T-ViT
        • CaiT
        • CCT
        • DeiT
        • SSA
        • SA3D
    • [NLP] Natural Language Processing
      • 01_[LLMs] Large Language Models
      • [MoEs] Mixture of Experts
      • LLM Techniques
      • Attention is All You Need
      • Positional Encoding
      • Tokenization
      • MICLe
    • [CV] Computer Vision
      • MLP-based Classification
        • MLP-Mixer
        • FNet
        • EANet
      • 01_[SL] Supervised Learning
        • 01_Classification
          • Convolution Variants
          • 1x1 Convolution
          • EfficientNetV2
          • ConvNeXtV2
        • 02_Detection
          • ConvMixer
          • SOLO
          • YOLOX
          • YOLOR
          • AugFPN
          • BoT_Cls
          • BoF_OD
          • YOLOv3
          • YOLOv4
          • YOLOv5
          • YOLOv6
          • YOLOv7
          • YOLOv8
          • YOLOv9
          • YOLO-NAS
          • TPH-YOLOv5
          • TPH-YOLOv5++
          • ViTDET
        • 03_Segmentation
          • Object Instance Survey 2022
          • 01_Instance Segmentation
          • 02_Semantic Segmentation
          • 03_Panoptic Segmentation
          • 04_3D Segmentation
          • 05_Unsupervised Segmentation
          • BMask RCNN
          • ISTR
          • Transfuse
        • 04_[IS] Interactive Segmentation
          • Interactive Segmentation Techniques
          • 02_3D Interactive Segmentation
          • 03_Video Object Segmentation
          • SAM
          • HA_SAM
          • CFR-ICL
          • MST
          • ECONet
          • SimpleClick
          • FocusCut
          • f-BRS
          • iSegformer
        • 05_Object Tracking
          • 00_ObjectTracking
          • Sort
          • DeepSort
          • FairMOT
          • ByteTrack
          • StrongSORT
          • Tracktor
          • JDE
          • CenterTrack
          • PermaTrack
          • TransTrack
          • TrackFormer
          • BoT-SORT
        • 06_Face Recognition
        • 07_Image Stitching
        • 08_Image Restoration
        • 06_Refinement
          • BPR
        • 10_Scene Understanding
          • CPNet
        • 11_Human Pose Estimation
          • 3D Human Pose
          • Human Pose
        • 12_[SR] Super Resolution
          • Bicubic++
        • 13_VideoPropagation
        • 14_Image Mating
        • 15_Knowledge Distillation
        • 16_Others
      • 02_[UL] Unsupervised Learning
        • 00_Unsupervised Learning
        • 02_Deep Clustering
          • 00_K_Clusters Decision
          • Deep Cluster
          • Cluster Fit
          • DEC
          • Improving Relational Regularized Autoencoders with Spherical Sliced Fused G
          • Taxanomy
          • DeepDPM
          • BCL
          • VaDE
          • t-SNE
          • Tree-SNE
        • 04_Diffusional Models
      • 03_[SSL] Self-Supervised Learning
        • 00_Self-Supervised Learning
        • 01_Contrastive Learning
          • CPC
          • DIM
          • CMC
          • AMDIM
          • SimCLR
          • MoCo
          • MoCov2
          • YADIM
          • VICReg
          • CSL
          • Towards Domain-Agnostic Contrastive Learning
          • Non-Parametric Instance Discrimination
          • Video Contrastive Learning with Global Context
          • SupCon
          • Barlow Twin
        • 02_Predictive Tasks
        • 03_Bootstrapping
          • BYOL
        • 04_Regularization
        • 05_Masked Image Models
          • Patch Localization
          • MAE
          • SimMIM
          • DINO
        • 06_Pretext Tasks
          • PIRL
        • 07_Clustering-based
          • SwAV
      • 04_Semi-Supervised Learning
        • Fully-/Semi-/Weakly-/ Learning
        • 01_Self-training
          • Pseudo-label
          • Noisy Student
        • 02_Consistency Regularization
          • Temporal Ensembling
          • Mean Teacher
          • VAT
          • UDA
        • 03_Hybrid Methods
          • MixUp
          • MixMatch
          • ReMixMatch
          • FixMatch
          • FixMatch (unmerge)
      • 05_Multi-learning Paradigm
        • 00_Multi-learning
        • 01_Multitask
        • Gradient Surgery
        • EtE Multi-task Learning with Attention
        • MTL for Dense Predictions
        • MTL using Uncertainty
        • Which Task learned together
        • GradNorm
        • OM-Net
        • 06_Multi-task Learning
      • 06_Generative Models
        • 00_Generative Models
        • 01_Autoencoders
          • AE vs Others
          • Sparse AE
          • Denoising AE
          • Contractive AE
          • Variational AE
          • DELG
        • 02_GAN
      • Graph Convolutional Networks
        • 00_Graph Convolutional Networks
      • Neural Radiance Fields (NeRFs)
      • Deep Belief Networks
    • Multimodal Models
    • Bag of Freebies - BOF
      • 01_Augmentation
        • Mosaic
        • Cut Out
        • Mix Up
      • 02_Loss Functions
        • 01_Classification Loss
        • 02_Segmentation Loss
        • 03_Object Detection Loss
        • 04_Self-Supervised Loss
        • 05_Interactive Segmentation Loss
      • 03_Optimizer
      • 04_Normalization
        • 00_Normalization
      • 05_Regularization
      • 06_Label Assignment
        • 00_Label Assignment
        • OTA
        • SimOTA
      • 07_Auxiliary Head
    • Bag of Specials - BoS
      • Feature Pyramid
        • RCNet
      • Receptive Field
      • Attention
        • 00_Attention Modules
        • SENet
        • CBAM
        • DANet
        • SDANet
        • AttaNet
        • HaloNets
        • GCNet
        • DeepSquare
        • LBAM
        • External-Attention
        • PCT
        • Residual Attention
        • DCANet
        • GANet
        • Triplet Attention
        • Lambda Networks
        • ACTION
        • VAN
        • SegNeXt
      • Local-/Global- Features
        • Unifying Nonlocal Blocks for Neural Networks
        • Local Features
        • Global Features
      • Activation Functions
        • SiLU dSiLU
      • Post-Processing
        • Soft-NMS
        • NMW
        • WBF
      • Sliding Window
      • Graph Networks
      • Feature Fusion/Integration
      • Data-Centric
    • Others
      • Selected Top-Conference Papers
        • AAAI2021_Papers
        • CVPR2021_Papers
        • ECCV2020_Papers
        • ICCV2021_Papers
        • ICLM2022_Papers
      • Cheat Sheets
        • Pandas
      • Conference Schedule
  • Data Science
    • 03_DS_Discrete Distribution
    • Data Scientist Professional
      • 3. Statistical Experimentation Theory
      • 4. Statistical Experimentation in Python
      • 5. Model development in Python
      • 7. Data Management in SQL
    • Data...
    • ETL
    • Airflow
  • Cloud Computing
    • Azure Data Fundamental
    • Amazon Web Services
      • AWS - Cloud 101
      • AWS - Machine Learning Foundation (Lab)
        • 1. Introduction to MLF
        • 2. AI and ML
        • 3. ML Pipeline
        • 4. ML Tools and Services
        • 5. Wrapping it Up
      • AWS - Cloud Practitioner Essentials
      • AWS - GenAI
    • Google Cloud
    • IBM Watson
  • Big Data
    • PySpark
      • Introduction to PySpark
        • 1. Getting to know PySpark
        • 2. Manipulating Data
        • 3. Getting Started with ML Pipelines
        • 4. Model Tuning and Selection
      • Big Data Fundamentals with PySpark
        • 1. Introduction to BigData Analysis with Spark
        • 2. Programming in PySpark RDD’s
        • 3. PySpark SQL & DataFrames
        • 4. Machine Learning with PySpark MLlib
  • English
    • Reading
    • Listening
    • Speaking
      • Speaking_Part1
        • 1_Speaking Part 1
        • 2_Speaking Part 1
        • 3_Speaking Part 1
        • 4_Speaking Part 1
        • 5_Speaking Part 1
        • 6_Speaking Part 1
        • 7_Speaking Part 1
        • 8_Speaking Part 1
        • 9_Speaking Part 1
        • 10_Speaking Part 1
        • 11_Speaking Part 1
        • 12_Speaking Part 1
        • 13_Speaking Part 1
        • 14_Speaking Part 1
        • 15_Speaking Part 1
        • 16_Speaking Part 1
        • 17_Speaking Part 1
        • 18_Speaking Part 1
        • 19_Speaking Part 1
        • 20_Speaking Part 1
        • 21_Speaking Part 1
        • 22_Speaking Part 1
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f-BRS

{}

[Paper] [Code]

0) Motivation, Object and Related works:

Motivation:


Objectives:


Loss function

  • Component:

    1. Inertial Function: regularization

    2. Corrective energy:  loss function

  • Purpose: Refine Scale S and Bias B

    1. Initial: S: 1; B:0

References:


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