深度神經網絡優化（一）- Practical aspects of Deep Learning
從本篇開始將是 deeplearning.ai的第二門課程，深度神經網絡的性能優化 Setting up your Machine Learning Application Train/Dev/Test sets 在機器學習中我們也講過關於數據集劃分的問題，通常我們會選擇將我們收集到的數據劃分爲訓練集，驗證集（Dev），測試集，訓練集用於模型訓練，驗證集用於超參數的設定以及模型問題的檢驗，測試集用
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相關文章
- 1. [改善深度神經網絡] Practical aspects of deep learning習題解析
- 2. 007 Practical aspects of Deep Learning
- 3. Deep learning II - I Practical aspects of deep learning - Normalizing inputs 輸入歸一化
- 4. Deep learning II - I Practical aspects of deep learning - Regularizing your neural network 神經網絡範數正則化
- 5. [coursera/ImprovingDL/week1]Practical aspects of Deep Learning(summary&question)
- 6. 【吳恩達深度學習筆記】2.1 Practical aspects of Deep Learning
- 7. Deep learning II - I Practical aspects of deep learning - Vanishing/Exploring gradients 梯度消失/爆炸
- 8. Deep learning II - I Practical aspects of deep learning - 正則化如何較少過擬合
- 9. Deep learning II - I Practical aspects of deep learning - Dropout regularization 隨機失活正則化
- 10. Deep learning II - I Practical aspects of deep learning - Understanding dropout 理解隨機失活正則化