心音-深度学习 该项目旨在在低功耗ARM处理器（例如在树莓派上找到的处理器）上运行。 目的是将该软件打包到一个小型硬件设备中，发展中国家的护理工作者可以使用该设备来检测心脏病的早期发作。

Thank you for purchasing the MEAP for Deep Learning with R. If you are looking for a resource to learn about deep learning from scratch and to quickly become able to use this knowledge to solve real-world problems, you have found the right book. Deep Learning with R is meant for statisticians, analysts, engineers and students with a reasonable amount of R experience, but no significant knowledge of machine learning and deep learning. This book is an adaptation of my previously published Deep Learning with Python, with all of the code examples using the R interface to Keras. The goal of the book is to provide a learning resource for the R community that goes all the way from basic theory to advanced practical applications. Deep learning is an immensely rich subfield of machine learning, with powerful applications ranging from machine perception to natural language processing, all the way up to creative AI. Yet, its core concepts are in fact very simple. Deep learning is often presented as shrouded in a certain mystique, with references to algorithms that “work like the brain”, that “think” or “understand”. Reality is however quite far from this science- fiction dream, and I will do my best in these pages to dispel these illusions. I believe that there are no difficult ideas in deep learning, and that’s why I started this book, based on premise that all of the important concepts and applications in this field could be taught to anyone, with very few prerequisites. This book is structured around a series of practical code examples, demonstrating on real- world problems every the notions that gets introduced. I strongly believe in the value of teaching using concrete examples, anchoring theoretical ideas into actual results and tangible code patterns. These examples all rely on Keras, the deep learning library. When I released the initial version of Keras almost two years ago, little did I know that it would quickly skyrocket to become one of the most widely used deep learning frameworks. A big part of that success is that Keras has always put ease of use and accessibility front and center. This same reason is what makes Keras a great library to get started with deep learning, and thus a great fit for this book. By the time you reach the end of this book, you will have become a Keras expert. I hope that you will this book valuable —deep learning will definitely open up new intellectual perspectives for you, and in fact it even has the potential to transform your career, being the most in-demand scientific specialization these days. I am looking forward to your reviews and comments. Your feedback is essential in order to write the best possible book, that will benefit the greatest number of people.

The Deep Learning textbook is a resource intended to help students and practitioners enter the field of machine learning in general and deep learning in particular. Table of Contents Chapter 1 Introduction Part I: Applied Math and Machine Learning Basics Chapter 2 Linear Algebra Chapter 3 Probability and Information Theory Chapter 4 Numerical Computation Chapter 5 Machine Learning Basics Part II: Modern Practical Deep Networks Chapter 6 Deep Feedforward Networks Chapter 7 Regularization Chapter 8 Optimization for Training Deep Models Chapter 9 Convolutional Networks Chapter 10 Sequence Modeling: Recurrent and Recursive Nets Chapter 11 Practical Methodology Chapter 12 Applications Part III: Deep Learning Research Chapter 13 Linear Factor Models Chapter 14 Autoencoders Chapter 15 Representation Learning Chapter 16 Structured Probabilistic Models for Deep Learning Chapter 17 Monte Carlo Methods Chapter 18 Confronting the Partition Function Chapter 19 Approximate Inference Chapter 20 Deep Generative Models

平滑分类器认证稳健性的一致性正则化 (NeurIPS2020) 该存储库包含和的论文“平滑分类器的证明稳健性的一致性正则化”代码。 依存关系 conda create -n smoothing-consistency python=3 conda activate smoothing-consistency # IMPORTANT: Please make sure `pytorch != 1.4.0` # Currently, our code is not compatible to `pytorch == 1.4.0`; # See more details at `https://github.com/pytorch/pytorch/issues/32395`. # Below is for linux, with CUDA 10; see https://pytorc

该压缩包含有TensorFlow0.12.0版本的Windows操作系统下载,并且含有下载安装教程

这是 ShowMeAI 持续分享的速查表系列！很多同学都是看吴恩达 Andrew Ng 的视频学习机器学习和深度学习的，当然学习就要做笔记。 Tess Ferrandez 分享了一套自己的课程笔记，很好地总结了学习内容，共28张精辟的手绘图。这应该是传播最广的笔记速查表之一，内容非常丰富！

PointNet2用于3D点云的语义分割 马蒂厄·奥罕（Mathieu Orhan）和纪尧姆·迪基瑟（Guillaume Dekeyser）着（巴黎桥和歌剧院，2018年，巴黎）。 介绍 这个项目是PointNet2的学生分支，由斯坦福大学的Charles R. Qi，Li（Eric）Yi，Hao Su，Leonidas J. Guibas提供。 有关详细信息，您可以参考原始的PointNet2论文和代码（ ）。 该分支专注于语义分割，目的是比较三个数据集：Scannet，Semantic-8和Bertrand Le Sa​​ux空中LIDAR数据集。 为此，我们清理，记录，重构和改进原始项目。 稍后，我们将把相同的数据集与另一个最新的语义分割项目SnapNet进行比较。 相关性和数据 我们使用3 GTX Titan Black和GTX Titan X在Ubuntu 16.04上工作。

用Python QuantStats编写的量化工具的投资组合分析：量化工具的投资组合分析QuantStats Python库执行投资组合分析，通过向量化分析人员和投资组合管理人员提供深入的分析和风险度量，可以更好地了解其绩效。 Changelog»QuantStats包含3个主要模块：quantstats.stats-用于计算各种性能指标，例如夏普比率，获胜率，波动率等。quantstats.plots-用于可视化性能，缩编，滚动统计，每月报告

论文Deep Learning-Based Channel Estimation for Beamspace mmWave Massive MIMO Systems源码。 这是Python版本的源码。 适合人群：人工智能、通信类研究人员。

人体姿态检测总结，Deep Learning-Based Human Pose Estimation: A Survey