[[["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 2024-11-08 UTC"],[[["Backpropagation is the primary training algorithm for neural networks, enabling gradient descent for multi-layer networks and often handled automatically by machine learning libraries."],["Vanishing gradients occur when gradients in lower layers become very small, hindering their training, and can be mitigated by using ReLU activation function."],["Exploding gradients happen when large weights cause excessively large gradients, disrupting convergence, and can be addressed with batch normalization or lowering the learning rate."],["Dead ReLU units emerge when a ReLU unit's output gets stuck at 0, halting gradient flow, and can be avoided by lowering the learning rate or using ReLU variants like LeakyReLU."],["Dropout regularization is a technique to prevent overfitting by randomly dropping unit activations during training, with higher dropout rates indicating stronger regularization."]]],[]]
