추천 시스템: 조회, 채점, 순위 재지정 (Retrieval, Scoring, Re-ranking)
Retrieval, Scoring, Re-ranking
Retrieval, Scoring, Re-ranking
Softmax Model, Softmax Training
Basics, Matrix Factorization, Advantages & Disadvantages
Basics, Advantages & Disadvantages
Overview
What and Why?, Terminology, Overview
Types of Bias, Identifying Bias, Evaluating for Bias
Raliability, Versioning, Necessity, Correlations, Feedback Loops
Static vs. Dynamic Training and Inference
Collaborative Filtering, Input Data, Translating, Obtaining
One vs. All, Softmax
Best Practices
Structure : Hidden Layers, Activation Functions
L1 Regularization
Threshold, True-False, Positive-Negative, Accuracy, Precision, Recall, ROC, AUC, Prediction Bias
Calculating a Probability, Loss and Regularization
L2 Regularization, Lambda
Encoding Nonlinearity, Crossing One-Hot Vectors
Feature Engineering, Qualities of Good Features, Cleaning Data
Training, Validation and Test Dataset
Peril of Overfitting
Iterative Approach, Gradient Desent, Learning Rate
Linear Regression, Training, Loss
Cloud Shell, Gcloud
Windows server, Remote Desktop Protocol
Cloud Consol, Cloud Shall
그리디 알고리즘(탐욕 알고리즘) 예제 풀기
그리디 알고리즘(탐욕 알고리즘)의 개념잡기