Recent developments in laser scanning technologies have provided innovative solutions for acquiring three-dimensional (3D) point clouds about road corridors and its environments. Unlike traditional field surveying, satellite imagery, and aerial photography, laser scanning systems offer unique solutions for collecting dense point clouds with millimeter accuracy and in a reasonable time. The data acquired by laser scanning systems empower modeling road geometry and delineating road design parameters such as slope, superelevation, and vertical and horizontal alignments. These geometric parameters have several geospatial applications such as road safety management. The purpose of this book is to promote the core understanding of suitable geospatial tools and techniques for modeling of road traffic accidents by the state-of-the-art artificial intelligence (AI) approaches such as neural networks (NNs) and deep learning (DL) using traffic information and road geometry delineated from laser scanning data. Data collection and management in databases play a major role in modeling and developing predictive tools. Therefore, the first two chapters of this book introduce laser scanning technology with creative explanation and graphical illustrations and review the recent methods of extracting geometric road parameters. The third and fourth chapters present an optimization of support vector machine and ensemble tree methods as well as novel hierarchical object-based methods for extracting road geometry from laser scanning point clouds. Information about historical traffic accidents and their circumstances, traffic (volume, type of vehicles), road features (grade, superelevation, curve radius, lane width, speed limit, etc.) pertains to what is observed to exist on road segments or road intersections. Soft computing models such as neural networks are advanced modeling methods that can be related to traffic and road features to the historical accidents and generates regression equations that can be used in various phases of road safety management cycle. The regression equations produced by NN can identify unsafe road segments, estimate how much safety has changed following a change in design, and quantify the effects of road geometric features and traffic information on road safety. This book aims to help graduate students, professionals, decision makers, and road planners in developing better traffic accident prediction models using advanced neural networks.

在下一篇文章中，我们将预处理要输入到机器学习模型的数据集。

训练12小时后512x512鲜花，1 gpu 训练12小时后256x256朵鲜花，1 gpu 比萨 ``轻巧''GAN 在Pytorch的ICLR 2021中提出的实现。 本文的主要贡献是发生器中的跳层激励，以及鉴别器中的自动编码自监督学习。 引用单行摘要“在经过数小时培训的情况下，可以在1024 g分辨率的数百张图像上融合在单个gpu上”。 安装 $ pip install lightweight-gan 使用 一个命令 $ lightweight_gan --data ./path/to/images --image-size 512 每隔1000次迭代，模型将保存到./models/{name} ，模型中的样本将保存到./results/{name} 。 name将是default ，默认情况下。 训练设定 深度学习从业人员的自我解释能力很强 $ lightweight_ga

深度学习中RBM的Matlab代码工具包，帮助更好的理解Deep learning

DeepSpeech：DeepSpeech是一种开源嵌入式（离线，设备上的）语音到文本引擎，可以在从Raspberry Pi 4到大功率GPU服务器的各种设备上实时运行

用于学习分子图的分层消息间传递 这是用于学习分子图的分层消息间传递的 PyTorch 实现，如我们的论文中所述： Matthias Fey、Jan-Gin Yuen、Frank Weichert：(GRL+ 2020) 要求 (>=1.4.0) (>=1.5.0) (>=1.1.0) 实验 可以通过以下方式运行实验： $ python train_zinc_subset.py $ python train_zinc_full.py $ python train_hiv.py $ python train_muv.py $ python train_tox21.py $ python train_ogbhiv.py $ python train_ogbpcba.py 引用 如果您在自己的工作中使用此代码，请引用： @inproceedings{Fey/etal/2020,

对 Deep convolutional network cascade for facial point detection[CVPR13]一文的复现，可演示的可执行文件。 目前只实现了第一层。

超网络 适用于ResNet的PyTorch实施（Ha等人，ICLR 2017）。该代码主要用于CIFAR-10和CIFAR-100，但是将其用于任何其他数据集都非常容易。将其用于不同深度的ResNet架构也非常容易。 我们使用pytorch闪电来控制整个管道。 怎么跑 python train.py --dataset {cifar10/cifar100} --gpus $num_gpu -j $num_workers --distributed_backend ddp 已过期。此回购协议已将HyperNet修改为逐层实施，使用起来更加方便。需要注意的是，我们发现尽管作者设置了in_size和out_size 。实际上，您应该将in_size和out_size设置为16，否则将失败。

Deep Learning with PyTorch,介绍了PyTorch在深度学习上的应用。

预测Covid 深度学习模型，用于使用X射线图像检测Covid-19。这是一个简单的分类模型，基线准确度为94％。 严谨 以下命令将根据配置文件requirements.txt安装所有必需的软件包。 pip install -r requirements.txt 要运行该应用程序，请使用以下命令 streamlit run app.py