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[ 机器学习 - 吴恩达 ] | 1-3 Supervised Learning

Housing price prediction Supervised Learning: "right answers" given. Regression: Predict continuous (连续的) valued output (price) Breast cancer (malignant (恶性的), benign (良性的)) Classification: Dicrete (离散的) valued output (0 or 1) 肿瘤大小是判断癌

30:无监督学习-自编码原理

1：自编码原理   【注】无监督学习的数据是没有标签的一类数据。  【注】自编码原理实际就是数据通过网络训练（升维以及降维），重够数据本身。  【注】MINiST数据集就是binary input类型的输入，其也属于real-valued input。但是real-valued input不一定属于binary input类型的输入。

[LeetCode] 1315. Sum of Nodes with Even-Valued Grandparent

Given a binary tree, return the sum of values of nodes with even-valued grandparent.  (A grandparent of a node is the parent of its parent, if it exists.) If there are no nodes with an even-valued grandparent, return 0. Example 1: Input: root = [6,7,8,2,

1315. Sum of Nodes with Even-Valued Grandparent

Given a binary tree, return the sum of values of nodes with even-valued grandparent.  (A grandparent of a node is the parent of its parent, if it exists.) If there are no nodes with an even-valued grandparent, return 0.   Example 1: Input: root = [6,7

Complex-Valued CNN and Its Application in Polarimetric SAR Image Classification

这里写目录标题 AbstractMethodResultsNote Abstract Following the great success of deep convolutional neural networks (CNNs) in computer vision, this paper proposes a complex-valued CNN (CV-CNN) specifically for synthetic aperture radar (SAR) image int

Complex-Valued CNN and Its Application in Polarimetric SAR Image Classification

Following the great success of deep convolutional neural networks (CNNs) in computer vision, this paper proposes a complex-valued CNN (CV-CNN) sp ally for synthetic aperture radar (SAR) image interpretation. It utilizes both amplitude and phase informati