Additional information
by John Magic (Editor), Mark Magic (Author)
This book implemented six different algorithms to classify images with the prediction accuracy of the testing data as the primary criterion (the higher the better) and the time consumption as the secondary one (the shorter the better). The accuracies varied between about 30% and 90%, while the time consumptions varied from several seconds to more than one hour. Considering both of the criteria, the Pre-Trained AlexNet Features Representation plus a Classifier, such as the k-Nearest Neighbors (KNN) and the Support Vector Machines (SVM), was concluded as the best algorithm.The six algorithms are: Tiny Images Representation + Classifiers; HOG (Histogram of Oriented Gradients) Features Representation + Classifiers; Bag of SIFT (Scale Invariant Feature Transform) Features Representation + Classifiers; Training a CNN (Convolutional Neural Network) from scratch; Fine Tuning a Pre-Trained Deep Network (AlexNet); and Pre-Trained Deep Network (AlexNet) Features Representation + Classifiers.The codes were written with Python in Jupyter Notebook, and they could be executed on both CPUs and GPUs.本书使用了六种不同的算法来对图像进行分类。其中测试数据的预测准确度为主要标准（越高越好），所花费的时间为次要标准（越短越好）。预测准确度大约在30％和90％之间变化，而所花费的时间从几秒钟到一个多小时不等。同时考虑这两个标准，预训练的 AlexNet 特征表示加上分类器，例如k个最近邻（KNN）和支持向量机（SVM），被认为是最佳的算法。这六种算法分别是：微小图像表示+分类器；方向梯度直方图（HOG）特征表示+分类器；尺度不变特征变换（SIFT）口袋特征表示+分类器；从头训练卷积神经网络（CNN）；微调预训练的深度网络（AlexNet）；以及预训练的深度网络（AlexNet）特征表示+分类器。这些代码全部用 Python 编写，并在 Jupyter Notebook 中运行。这些代码都可以运行在 CPU 和 GPU 上。
