This article is my reflection on my previous work FaceLock, a project to recognize user's face and lock the computer if the user doesn't present in a certain time. CNN is used to recognize different faces. I watch the Coursera course Convolutional Neural Networks by Andrew Ng to understand more about CNN, so it's also a learning note about it. One Layer of a Convolutional Network In a non-convolutional network, we have the following formula: Similarly, in the convolutional network, we can have: @ is a convolution operation. @ is the input matrix. @ is the filter. Different filter can detect different feature, e.g. vertical edge, diagonal edge, etc. @ is the bias. @ is a activation function. @ is the output matrix, and can be fed to the next layer. Calculating the Number The Number of the Parameters Suppose we have 10 filters which are in one layer of a neural

Python 实现: AdaBoost - Donny-Hikari - Github Introduction AdaBoost 是 Adaptive Boosting 的简称。 Boosting 是一种 Ensemble Learning 方法。 其他的 Ensemble Learning 方法还有 Bagging, Stacking 等。 Bagging, Boosting, Stacking 的区别如下： Bagging: Equal weight voting. Trains each model with a random drawn subset of training set. Boosting: Trains each new model instance to emphasize the training instances that previous models mis-classified. Has better accuracy comparing to bagging, but also tends to overfit. Stacking: Trains a learning algorithm to combine the predictions of several other learning algorithms. The Formulas Given a N*M matrix X, and a N vector y, where N is the count of samples, and M is the features of samples. AdaBoost trains T weak classifiers with the following steps: 给定一个N*M的矩阵X（特征），和一个N维向量y（标签），N为样本数，M为特征维度。AdaBoost以一下步骤训练T个弱分类器：