2026 [15]

• Recommendation Systems (1): Fundamentals and Core Concepts 02-03
• Recommendation Systems (10): Deep Interest Networks and Attention Mechanisms 02-03
• Recommendation Systems (11): Contrastive Learning and Self-Supervised Learning 02-03
• Recommendation Systems (12): Large Language Models and Recommendation 02-03
• Recommendation Systems (13): Fairness, Debiasing, and Explainability 02-03
• Recommendation Systems (14): Cross-Domain Recommendation and Cold-Start Solutions 02-03
• Recommendation Systems (15): Real-Time Recommendation and Online Learning 02-03
• Recommendation Systems (16): Industrial Architecture and Best Practices 02-03
• Recommendation Systems (2): Collaborative Filtering and Matrix Factorization 02-03
• Recommendation Systems (3): Deep Learning Foundation Models 02-03
• Recommendation Systems (4): CTR Prediction and Click-Through Rate Modeling 02-03
• Recommendation Systems (5): Embedding and Representation Learning 02-03
• Recommendation Systems (8): Knowledge Graph-Enhanced Recommendation 02-03
• Recommendation Systems (7): Graph Neural Networks and Social Recommendation 02-03
• Recommendation Systems (6): Sequential Recommendation and Session-based Modeling 02-03

2025 [12]

• Transfer Learning (7): Zero-Shot Learning 11-15
• Mathematical Derivation of Machine Learning (5): Linear Regression 10-25
• Mathematical Derivations in Machine Learning (7): Decision Trees 10-05
• Machine Learning Math Derivations (15): Hidden Markov Models 08-15
• Machine Learning Math Derivations (16): Conditional Random Fields 05-05
• LLM Workflows and Application Architecture: Enterprise Implementation Guide 04-05
• AI Agents Complete Guide: From Theory to Industrial Practice 04-03
• Prompt Engineering Complete Guide: From Zero to Advanced Optimization 04-01
• Mathematical Derivations in Machine Learning (6): Logistic Regression and Classification 02-15
• Mathematical Derivations in Machine Learning (1): Introduction and Mathematical Foundations 01-15
• Transfer Learning (12): Industrial Applications and Best Practices 01-08
• Transfer Learning (11): Cross-Lingual Transfer 01-02

2024 [51]

• Transfer Learning (10): Continual Learning 12-27
• Transfer Learning (9): Parameter-Efficient Fine-Tuning 12-21
• Transfer Learning (8): Multimodal Transfer 12-15
• Transfer Learning (6): Multi-Task Learning 12-03
• Transfer Learning (5): Knowledge Distillation 11-27
• Transfer Learning (4): Few-Shot Learning 11-21
• Transfer Learning (3): Domain Adaptation Methods 11-15
• Transfer Learning (2): Pre-training and Fine-tuning Techniques 11-09
• Transfer Learning (1): Fundamentals and Core Concepts 11-03
• Reinforcement Learning (12): RLHF and Large Language Model Applications 10-04
• Graph Contextualized Self-Attention Network (GC-SAN) for Session-based Recommendation 10-01
• Solving Constrained Mean-Variance Portfolio Optimization Problems Using Spiral Optimization Algorithm 10-01
• Session-based Recommendation with Graph Neural Networks (SR-GNN) 10-01
• paper2repo: GitHub Repository Recommendation for Academic Papers 10-01
• Reinforcement Learning (11): Hierarchical Reinforcement Learning and Meta-Learning 09-27
• Reinforcement Learning (10): Offline Reinforcement Learning 09-20
• Reinforcement Learning (9): Multi-Agent Reinforcement Learning 09-13
• Reinforcement Learning (8): AlphaGo and Monte Carlo Tree Search 09-06
• Reinforcement Learning (7): Imitation Learning and Inverse Reinforcement Learning 09-06
• Reinforcement Learning (6): PPO and TRPO - Trust Region Policy Optimization 09-03
• LLMGR: Integrating Large Language Models with Graphical Session-Based Recommendation 09-02
• Reinforcement Learning (5): Model-Based RL and World Models 08-30
• Reinforcement Learning (4): Exploration Strategies and Curiosity-Driven Learning 08-23
• Learning Rate: From Basics to Large-Scale Training (2026 Complete Guide) 08-20
• MoSLoRA: Mixture-of-Subspaces in Low-Rank Adaptation 08-19
• Reinforcement Learning (3): Policy Gradient and Actor-Critic Methods 08-16
• Time Series Models (8): Informer for Long Sequence Forecasting 08-16
• Reinforcement Learning (2): Q-Learning and Deep Q-Networks (DQN) 08-09
• Reinforcement Learning (1): Fundamentals and Core Concepts 08-02
• Time Series Models (7): N-BEATS Deep Architecture 07-23
• Time Series Models (6): Temporal Convolutional Networks (TCN) 06-30
• Recommendation Systems (9): Multi-Task Learning and Multi-Objective Optimization 06-11
• Time Series (5): Transformer Architecture 06-08
• Time Series Forecasting (4): Attention Mechanisms - Direct Long-Range Dependencies 05-18
• Time Series Forecasting (3): GRU - Lightweight Gates & Efficiency Trade-offs 04-25
• NLP (12): Frontiers and Practical Applications 04-11
• NLP (11): Multimodal Large Language Models 04-04
• Time Series Forecasting (2): LSTM - Gate Mechanisms & Long-Term Dependencies 04-02
• NLP (10): RAG and Knowledge Enhancement Systems 03-28
• NLP (9): Deep Dive into LLM Architecture 03-21
• NLP (8): Model Fine-tuning and PEFT 03-15
• NLP (7): Prompt Engineering and In-Context Learning 03-09
• Variational Autoencoder (VAE): From Intuition to Implementation and Troubleshooting 03-05
• NLP (6): GPT and Generative Language Models 03-03
• Kernel Methods: From Theory to Practice (RKHS, Common Kernels, and Hyperparameter Tuning) 02-28
• NLP (5): BERT and Pretrained Models 02-26
• Prefix-Tuning: Optimizing Continuous Prompts for Generation 02-26
• NLP (4): Attention Mechanism and Transformer 02-20
• NLP (3): RNN and Sequence Modeling 02-14
• NLP (2): Word Embeddings and Language Models 02-08
• NLP (1): Introduction and Text Preprocessing 02-03

2023 [30]

• HCGR: Hyperbolic Contrastive Graph Representation Learning for Session-based Recommendation 09-04
• Graph Neural Networks for Learning Equivariant Representations of Neural Networks 09-02
• LeetCode (9): Greedy Algorithms 08-22
• LeetCode (10): Stack and Queue 08-20
• LeetCode (8): Backtracking Algorithm 07-28
• LeetCode (7): Dynamic Programming Basics 07-06
• LeetCode (6): Binary Tree Traversal & Construction 06-20
• LeetCode (5): Binary Search 06-15
• LeetCode (4): Sliding Window Technique 05-29
• LeetCode (3): Linked List Operations - Reversal, Cycle Detection & Merging 05-08
• LeetCode (2): Two Pointers - Collision, Fast-Slow & Sliding Window Complete Guide 04-18
• LeetCode (1): Hash Tables - Patterns, Pitfalls & Three Classic Problems Deep Dive 03-25
• Cloud Computing (8): Multi-Cloud Management and Hybrid Architecture 03-20
• Computer Fundamentals (5): Network, Power & Practical Troubleshooting - Ultimate Guide from Hardware to Diagnostics 03-18
• Cloud Computing (7): Operations and DevOps Practices 03-08
• Computer Fundamentals (4): Motherboard, Graphics & Expansion - From Interface Protocols to GPU Parallel Computing 02-27
• Cloud Computing (6): Security and Privacy Protection 02-25
• Cloud Computing (5): Network Architecture and SDN 02-15
• Computer Fundamentals (3): Storage Systems - Complete Guide from HDD to SSD 02-14
• Cloud Computing (4): Cloud-Native and Container Technologies 02-05
• Cloud Computing (3): Storage Systems and Distributed Architecture 01-25
• Computer Fundamentals (2): Memory & High-Speed Cache Systems - Complete Guide from DDR Evolution to Dual-Channel Optimization 01-25
• Cloud Computing (2): Virtualization Technology Deep Dive 01-15
• Computer Fundamentals (1): CPU & Computing Core - Complete Guide from Data Units to Processor Architecture 01-08
• LAMP Stack on Alibaba Cloud ECS: From Fresh Instance to Production-Ready Web Server 01-07
• Linux Pipelines and Text Processing: Composing Tools into Data Flows 01-06
• Cloud Computing (1): Fundamentals and Architecture Systems 01-05
• Vim Essentials: Modal Editing, Motions, and a Repeatable Workflow 01-05
• Linux Process and Resource Management: Monitoring, Troubleshooting, and Optimization 01-04
• Linux Package Management: apt/dpkg, yum/dnf/rpm, Building from Source 01-02

2022 [21]

• Linux Service Management: systemd, systemctl, and journald Deep Dive 12-28
• Linux Disk Management: From Hardware to Filesystems (RAID, LVM, GPT/MBR, Mounting, and Recovery) 12-25
• Linux User Management: Users, Groups, UID/GID, sudo, and Password Policies 12-20
• Linux File Permissions: rwx, chmod/chown, umask, SUID/SGID/Sticky, and Troubleshooting 12-15
• Linux Basics: Core Concepts and Essential Commands 12-10
• PDE and Machine Learning (8): Reaction-Diffusion Systems and GNN 03-12
• PDE and Machine Learning (7): Diffusion Models and Score Matching 03-05
• PDE and Machine Learning (8) - Reaction-Diffusion Systems and GNN 03-01
• PDE and Machine Learning (7) - Diffusion Models and Score Matching 02-22
• PDE and Machine Learning (6): Continuous Normalizing Flows and Neural ODE 02-22
• PDE and Machine Learning (5): Symplectic Geometry and Structure-Preserving Networks 02-15
• PDE and Machine Learning (6) - Continuous Normalizing Flows and Neural ODE 02-15
• PDE and Machine Learning (5) - Symplectic Geometry and Structure-Preserving Networks 02-08
• PDE and Machine Learning (4): Variational Inference and Fokker-Planck Equation 02-05
• PDE and Machine Learning (4) - Variational Inference and Fokker-Planck Equation 02-01
• PDE and Machine Learning (3) - Variational Principles and Optimization 01-25
• PDE and Machine Learning (3): Variational Principles and Optimization 01-25
• PDE and Machine Learning (2) - Neural Operator Theory 01-18
• PDE and Machine Learning (2) — Neural Operator Theory 01-18
• PDE and Machine Learning (1) — Physics-Informed Neural Networks 01-10
• PDE and Machine Learning (1) - Physics-Informed Neural Networks 01-10

2021 [20]

• Machine Learning Mathematical Derivations (20): Regularization and Model Selection 12-17
• Machine Learning Mathematical Derivations (19): Neural Networks and Backpropagation 12-11
• Machine Learning Mathematical Derivations (18): Clustering Algorithms 12-05
• Machine Learning Mathematical Derivations (17): Dimensionality Reduction and PCA 11-29
• Machine Learning Mathematical Derivations (16): Conditional Random Fields 11-23
• Machine Learning Mathematical Derivations (15): Hidden Markov Models 11-17
• Machine Learning Mathematical Derivations (14): Variational Inference and Variational EM 11-11
• Machine Learning Mathematical Derivations (13): EM Algorithm and GMM 11-05
• Machine Learning Mathematical Derivations (12): XGBoost and LightGBM 10-30
• Machine Learning Mathematical Derivations (11): Ensemble Learning 10-24
• Machine Learning Mathematical Derivations (10): Semi-Naive Bayes and Bayesian Networks 10-18
• Machine Learning Mathematical Derivations (9): Naive Bayes 10-12
• Machine Learning Mathematical Derivations (8): Support Vector Machines 10-06
• Machine Learning Mathematical Derivations (7): Decision Trees 09-30
• Machine Learning Mathematical Derivations (6): Logistic Regression and Classification 09-24
• Machine Learning Mathematical Derivations (5): Linear Regression 09-18
• Machine Learning Mathematical Derivations (4): Convex Optimization Theory 09-12
• Machine Learning Mathematical Derivations (3): Probability Theory and Statistical Inference 09-06
• Machine Learning Mathematical Derivations (2): Linear Algebra and Matrix Theory 08-31
• Machine Learning Mathematical Derivations (1): Introduction and Mathematical Foundations 08-25

2020 [1]

• Time Series Models (1): Traditional Statistical Models 04-05

2019 [37]

• Computer Fundamentals (6): Deep Dive and Summary - Complete Guide from Fundamentals to System Integration 11-02
• Ordinary Differential Equations (18): Advanced Topics and Summary 06-30
• Ordinary Differential Equations (17): Physics and Engineering Applications 06-27
• Ordinary Differential Equations (16): Fundamentals of Control Theory 06-23
• Ordinary Differential Equations (15): Population Dynamics 06-19
• Ordinary Differential Equations (14): Epidemic Models and Epidemiology 06-14
• Ordinary Differential Equations (13): Introduction to Partial Differential Equations 06-09
• Ordinary Differential Equations (12): Boundary Value Problems 06-03
• Ordinary Differential Equations (11): Numerical Methods 05-29
• Ordinary Differential Equations (10): Bifurcation Theory 05-24
• Ordinary Differential Equations (9): Chaos Theory and the Lorenz System 05-19
• Ordinary Differential Equations (8): Nonlinear Systems and Phase Portraits 05-13
• Ordinary Differential Equations (7): Stability Theory 05-07
• Ordinary Differential Equations (6): Linear Systems of Differential Equations 05-01
• Ordinary Differential Equations (5): Series Solutions and Special Functions 04-25
• Ordinary Differential Equations (4): The Laplace Transform 04-19
• Ordinary Differential Equations (3): Higher-Order Linear Equations 04-14
• Ordinary Differential Equations (2): First-Order Equations 04-08
• Ordinary Differential Equations (1): Origins and Intuition 04-03
• Essence of Linear Algebra (18): Frontiers and Summary 03-30
• Essence of Linear Algebra (17): Linear Algebra in Computer Vision 03-26
• Essence of Linear Algebra (16): Linear Algebra in Deep Learning 03-22
• Essence of Linear Algebra (15): Linear Algebra in Machine Learning 03-18
• Essence of Linear Algebra (14): Random Matrix Theory 03-14
• Essence of Linear Algebra (13): Tensors and Multilinear Algebra 03-09
• Essence of Linear Algebra (12): Sparse Matrices and Compressed Sensing 03-04
• Essence of Linear Algebra (11): Matrix Calculus and Optimization 02-28
• Essence of Linear Algebra (10): Matrix Norms and Condition Numbers 02-23
• Essence of Linear Algebra (9): Singular Value Decomposition 02-17
• Essence of Linear Algebra (8): Symmetric Matrices and Quadratic Forms 02-12
• Essence of Linear Algebra (7): Orthogonality and Projections 02-06
• Essence of Linear Algebra (6): Eigenvalues and Eigenvectors 02-01
• Essence of Linear Algebra (5): Linear Systems and Column Space 01-26
• Essence of Linear Algebra (4): The Secrets of Determinants 01-20
• Essence of Linear Algebra (3): Matrices as Linear Transformations 01-15
• Essence of Linear Algebra (2): Linear Combinations and Vector Spaces 01-09
• Essence of Linear Algebra (1): The Essence of Vectors - More Than Just Arrows 01-05