Archives
- 05 Jan [추천시스템 논문 리뷰] Auto-Encoding Variational Bayes
- 05 Jan [추천시스템 논문 리뷰] CL4CTR: A Contrastive Learning Framework for CTR Prediction
- 05 Jan [추천시스템 논문 리뷰] MMGCN : Multi-modal Graph Convolution Network for Personalized Recommendation of Micro-video
- 05 Jan [추천시스템 논문 리뷰] Monolith : Real Time Recommendation System with Collisionless Embedding Table
- 04 Jan [추천시스템 논문 리뷰] Wide & Deep Learning for Recommender Systems
- 04 Jan [추천시스템 논문 리뷰] Factorization Machines
- 16 Nov All About Decision Trees - 의사결정나무의 모든 것
- 29 Oct [추천시스템 논문 리뷰] Matrix Factorization Techniques for Recommender Systems
- 14 Oct [DL Book] 11.5. Debugging Strategies
- 13 Oct [DL Book] 11.4. Selecting Hyperparameters
- 12 Oct [DL Book] 11.3. Determining Whether to Gather More Data
- 11 Oct [DL Book] 11.2. Default Baseline Models
- 10 Oct [DL Book] 11.1. Evaluation Metrics
- 09 Oct [DL Book] 11. Practical Methodologies
- 06 Oct [DL Book] 8.1. How Learning Differs from Pure Optimization
- 05 Oct [DL Book] 8. Optimization for Deep Learning
- 01 Oct [DL Book] 7-13. Adversarial Training
- 30 Sep [DL Book] 7-12. Dropout
- 29 Sep [DL Book] 7-11. Bagging and other Ensemble Methods
- 28 Sep [DL Book] 7-10. Sparse Representations
- 27 Sep [DL Book] 7-9. Parameter Tying and Parameter Sharing
- 26 Sep [DL Book] 7-8. Early Stopping
- 25 Sep [DL Book] 7-7. Multitask Learning
- 24 Sep [DL Book] 7-5. Noise Robustness
- 23 Sep [DL Book] 7-4. Data Augmentation
- 22 Sep [DL Book] 7-3. Regularization and Under-Constrained Problems
- 21 Sep [DL Book] 7-1-1. Parameter Norm Penalties, L1 Regularization
- 20 Sep [DL Book] 7-1-1. Parameter Norm Penalties, L2 Regularization
- 19 Sep [DL Book] 7. Regularization for Deep Learning