[[["เข้าใจง่าย","easyToUnderstand","thumb-up"],["แก้ปัญหาของฉันได้","solvedMyProblem","thumb-up"],["อื่นๆ","otherUp","thumb-up"]],[["ไม่มีข้อมูลที่ฉันต้องการ","missingTheInformationINeed","thumb-down"],["ซับซ้อนเกินไป/มีหลายขั้นตอนมากเกินไป","tooComplicatedTooManySteps","thumb-down"],["ล้าสมัย","outOfDate","thumb-down"],["ปัญหาเกี่ยวกับการแปล","translationIssue","thumb-down"],["ตัวอย่าง/ปัญหาเกี่ยวกับโค้ด","samplesCodeIssue","thumb-down"],["อื่นๆ","otherDown","thumb-down"]],["อัปเดตล่าสุด 2024-11-14 UTC"],[[["\u003cp\u003eSimpler models often generalize better to new data than complex models, even if they perform slightly worse on training data.\u003c/p\u003e\n"],["\u003cp\u003eOccam's Razor favors simpler explanations and models, prioritizing them over more complex ones.\u003c/p\u003e\n"],["\u003cp\u003eRegularization techniques help prevent overfitting by penalizing model complexity during training.\u003c/p\u003e\n"],["\u003cp\u003eModel training aims to minimize both loss (errors on training data) and complexity for optimal performance on new data.\u003c/p\u003e\n"],["\u003cp\u003eModel complexity can be quantified using functions of model weights, like L1 and L2 regularization.\u003c/p\u003e\n"]]],[],null,[]]
