Feed Forward and Backward Run in Deep Convolution Neural Network 論文閱讀筆記
徒手實現CNN：綜述論文詳解卷積網絡的數學本質 Abstract 對卷積網絡的數學本質和過程仍然不是太清楚，這也就是本論文的目的。 我們使用灰度圖作爲輸入信息圖像， ReLU 和 Sigmoid 激活函數構建卷積網絡的非線性屬性， 交叉熵損失函數用於計算預測值與真實值之間的距離。 1 Introduction 2 Architecture 2.1 卷積層 reference * [CNN學習筆記]
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