Deep Learning - Ian Goodfellow 英文清晰原版，整理于2017-10-31，有完整的书签, 并且对大小做了优化。

An_Introduction_to_Deep_Learning_for_the_Physical_Layer 翻译

cnn源码matlab SVHN-deep-cnn-digit-detector 该项目在自然场景中实现了 deep-cnn-detector（和识别器）。 我使用 keras 框架和 opencv 库来构建检测器。 该检测器使用 CNN 分类器为 MSER 算法提出的区域确定数字与否。 先决条件 Python 2.7 keras 1.2.2 opencv 2.4.11 张量流-GPU == 1.0.1 等等。 运行这个项目所需的所有包的列表可以在 . Python环境 我建议您创建和使用独立于您的项目的 anaconda 环境。 您可以按照以下简单步骤为该项目创建 anaconda env。 使用以下命令行创建 anaconda env： $ conda env create -f digit_detector.yml 激活环境$ source activate digit_detector 在这个环境中运行项目 用法 数字检测器的构建过程如下： 0. 下载数据集 下载 train.tar.gz 并解压文件。 1.加载训练样本（1_sample_loader.py） Svhn 以 m

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！

这里是 ShowMeAI 持续分享的【开源eBook】系列！内容覆盖机器学习、深度学习、数据科学、数据分析、大数据、Keras、TensorFlow、PyTorch、强化学习、数学基础等各个方向。整理自各平台的原作者公开分享（审核大大请放手） ◉ 简介：从2016年春季学期开始，作者 Jeff Heaton 开始为圣路易斯华盛顿大学教授 T81-558 深度学习的应用课程，并将课程材料、例子和作业放在 GitHub 上，逐渐丰富演变成了这本书。 ◉ 目录： 第1章：Python 预备课程 第2章：用于机器学习的 Python 第3章：TensorFlow 简介 第4章：表格数据训练 第5章：正则化和Dropout 第6章：用于计算机视觉的卷积神经网络 第7章：生成对抗网络 第8章：Kaggle 数据集 第9章：迁移学习 第10章：Keras 中的时间序列 第11章：Hugging Face的自然语言处理 第12章：强化学习 第13章：高级/其他主题 第14章：其他神经网络技术