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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
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
More
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
Open VINO
References:
https://classroom.udacity.com/courses/ud132
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