人脸检测

说明：  机器学习的一项作业，仅供参考，主要KNN、逻辑回归、SVM、mlp、cnn、朴素贝叶斯的分类器性能的比较(An assignment of machine learning for reference only)

说明：  Python与开源GIS——数据处理、空间分析与地图制图(Python and open source GIS: data processing, spatial analysis and Cartography)

我们提供一类称为MobileNets的高效模型，用于移动和嵌入式视觉应用。?MobileNets是基于一个流线型的架构，它使用深度可分离的卷积来构建轻量级的深层神经网络。我们引入两个简单的全局超参数，在延迟度和准确度之间有效地进行平衡。这两个超参数允许模型构建者根据问题的约束条件，为其应用选择合适大小的模型。我们进行了资源和精度权衡的广泛实验，与ImageNet分类上的其他流行的网络模型相比，MobileNets表现出很强的性能。最后，我们展示了MobileNets在广泛的应用场景中的有效性，包括物体检测，细粒度分类，人脸属性和大规模地理定位。(We provide an efficient model called MobileNets for mobile and embedded vision applications. MobileNets is based on a streamlined architecture that USES deep separable convolution to build a lightweight deep neural network. We introduce two simple global hyperparameters to effectively balance the delay and accuracy. These two hyperparameters allow the model builder to select an appropriate size model for its application based on the constraints of the problem. We conducted extensive experiments on resource and precision tradeoffs, and MobileNets showed strong performance compared with other popular network models on the ImageNet classification. Finally, we demonstrate the effectiveness of MobileNets in a wide range of application scenarios, including object detection, fine-grained classification, face attributes, and large-scale geographic localization.)

人工智能在银行领域应用

说明：  基于深度学习的深度神经网络融合的蛋白质预测位点算法(Protein prediction site algorithm based on deep learning and deep neural network fusion)

说明：  本代码采用python语言编写的的一个LSTM时间序列来预测销量(This code uses a LSTM time series written in Python language to predict sales)

This book is also about complexity science, which is an interdisciplinary field—at the intersection of mathematics, computer science and natural science—that focuses on discrete models of physical systems. In particular, it focuses on complex systems, which are systems with many interacting components.

用于abaqus周期性边界条件的建立，采用python参数化建模。(The periodic boundary conditions for ABAQUS,The parameterized modeling of Python is adopted.)

Python codes for community detection

说明：  本rar文件是图像超分辨率重构的网络模型RCAN的代码，可以成功运行(This rar file is the code of the image super-resolution reconstruction network model RCAN, which can be run successfully)