ENVI SARscape5.2.1 提示OpenCL无法安装的解决方案
安装SARscape5.2.1时,点击安装opencl CPU-only runtime,提示已有更新版本存在,但是点击确定后又提示没有可用的opencl CPU-only runtime。如果安装老版本的OpenCL,可能会提示有更新的版本存在,无法安装。解决:直接下载本资源文件,点击安装OpenCL,安装后,可运行!
安装SARscape5.2.1时,点击安装opencl CPU-only runtime,提示已有更新版本存在,但是点击确定后又提示没有可用的opencl CPU-only runtime。如果安装老版本的OpenCL,可能会提示有更新的版本存在,无法安装。解决:直接下载本资源文件,点击安装OpenCL,安装后,可运行!
2012版李航《统计学习方法》,带有完整目录 目录为个人制作,已经校对,无错误 需要注意,该版本的《统计机器学习》清晰度较高,但不是高清版本。阅读上不存在障碍,需要学习的请自行下载!
语义分割网络deeplabV1,V2,V3论文原文 DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs 于2016年6月2日提交到Arxiv https://arxiv.org/abs/1606.00915,使用了空洞卷积;提出了在空间维度上实现金字塔型的空洞池化atrous spatial pyramid pooling(ASPP);使用了全连接条件随机场。空洞卷积在不增加参数数量的情况下增大了感受野,按照上文提到的空洞卷积论文的做法,可以改善分割网络。我们可以通过将原始图像的多个重新缩放版本传递到CNN网络的并行分支(即图像金字塔)中,或是可使用不同采样率(ASPP)的多个并行空洞卷积层,这两种方法均可实现多尺度处理。我们也可通过全连接条件随机场实现结构化预测,需将条件随机场的训练和微调单独作为一个后期处理步骤。 后期的deeplabV2、V3都是在deeplabV1的基础上改进而来。
这篇文档内容的来源多样,既有来自于R 官方文档(包括R intro,R data,R admin),也有来自于互联网的contributed documents;还有若干来自于Capital Of Statistics 论坛的讨论问题。本文档的目的是为具有一定统计(数学)背景的R 软件初学者提供一个快速认识R 软件的平台,如果你无此背景,可能会对其中的若干表达存在疑问。这篇文档重点不在统计方法上,因此所列问题不可能详尽到统计学的每个知识点。R 是一个很庞大的体系,在CRAN 的Task Views 上可以清楚地看到贝叶斯推断、聚类分析、机器学习、空间统计、稳健统计等方法的介绍。而这些方法又通过相应的R Packages 扩展,可以说学习R 是一件没有尽头的事情。如果你的英文阅读没问题,那么精读一本关于R 的原版书籍也是一个不错的选择,但这个开 头常常让人很头痛。希望这份文档,对你认识、学习R 是个不错的帮助。
Key Features, Learn advanced techniques in deep learning with this example-rich guide on Google's brainchildExplore various neural networks with the help of this comprehensive guideAdvanced guide on machine learning techniques, in particular TensorFlow for deep learning., Book Description, Deep learning is the next step after machine learning. It is machine learning but with a more advanced implementation. As machine learning is no longer an academic topic, but a mainstream practice, deep learning has taken a front seat. With deep learning being used by many data scientists, deeper neural networks are evaluated for accurate results. Data scientists want to explore data abstraction layers and this book will be their guide on this journey. This book evaluates common, and not so common, deep neural networks and shows how these can be exploited in the real world with complex raw data using TensorFlow., The book will take you through an understanding of the current machine learning landscape then delve into TensorFlow and how to use it by considering various data sets and use cases. Throughout the chapters, you'll learn how to implement various deep learning algorithms for your machine learning systems and integrate them into your product offerings such as search, image recognition, and language processing. Additionally, we'll examine its performance by optimizing it with respect to its various parameters, comparing it against benchmarks along with teaching machines to learn from the information and determine the ideal behavior within a specific context, in order to maximize its performance., After finishing the book, you will be familiar with machine learning techniques, in particular TensorFlow for deep learning, and will be ready to apply some of your knowledge in a real project either in a research or commercial setting., What you will learn, Provide an overview of the machine learning landscapeLook at the historical development and progress of deep learningDescribe TensorFlow and become very familiar with it both in theory and in practiceAccess public datasets and use TF to load, process, clean, and transform dataUse TensorFlow on real-world data sets including images and textGet familiar with TensorFlow by applying it in various hands on exercises using the command lineEvaluate the performance of your deep learning modelsQuickly teach machines to learn from data by exploring reinforcement learning techniques.Understand how this technology is being used in the real world by exploring active areas of deep learning research and application.