个人学习——论文翻译同时+阅读

matlab指纹图像分割代码很棒的深度学习 目录 免费在线书籍 通过Yoshua Bengio，Ian Goodfellow和Aaron Courville（05/07/2015） 由Michael Nielsen（2014年12月） 由Microsoft Research（2013） 蒙特利尔大学LISA实验室（2015年1月6日） 由Andrej Karpathy撰写：基于numpy的RNN / LSTM实现 课程 吴安德（Andrew Ng）在Coursera（2010-2014） 由Yaser Abu-Mostafa（2012-2014） 作者：汤姆·米切尔（Tom Mitchell）（2011年Spring） 由杰弗里·欣顿（Geoffrey Hinton）在Coursera（2012）中 舍布鲁克大学（Universitéde Sherbrooke）的雨果·拉罗谢尔（Hugo Larochelle）（2013） 通过CILVR实验室@纽约大学（2014） 丹·克莱恩（Dan Klein）和彼得阿比尔（Pieter Abbeel）（2013） 帕特里克·亨利·温斯顿（Patr

PSO粒子群算法优化极限学习机ELM参数，即PSO-ELM

This book is written for two kinds of readers. The first type of reader is one who plans to study Deep Learning in a systematic approach for further research and development. This reader should read all the content from the beginning to end. The example code will be especially helpful for further understanding the concepts. A good deal of effort has been made to construct adequate examples and implement them. The code examples are constructed to be easy to read and understand. They are written in MATLAB for better legibility. There is no better programming language than MATLAB at being able to handle the matrices of Deep Learning in a simple and intuitive manner. The example code uses only basic functions and grammar, so that even those who are not familiar with MATLAB can easily understand the concepts. For those who are familiar with programming, the example code may be easier to understand than the text of this book. The other kind of reader is one who wants more in-depth information about Deep Learning than what can be obtained from magazines or newspapers, yet doesn’t want to study formally. These readers can skip the example code and briefly go over the explanations of the concepts. Such readers may especially want to skip the learning rules of the neural network.

Pytorch神经网络编程学习记录

Learning WebRTC随书配套代码。 WebRTC是一个支持网络浏览器进行实时语音对话或视频对话的软件架构。本书使用形象的案例介绍，逐步深入地阐述了 WebRTC 的幕后工作原理。通过阅读本书，读者可以快速、有效地掌握创建一个 WebRTC 应用所必需的知识，包括获取用户设备信息、创建 WebRTC 应用的客户端和服务器、连接用户并发送数据、文件共享、数据信息安全和性能优化。

Deep Learning (Ian Goodfellow, Yoshua Bengio and Aaron Courville)深度学习中英文版本资源-附件资源

本书可作为精益生产相关从业人员的研究、参考资料，也可以作为高等院校工业工程及相关专业本科生和研究生的教材。

OpenJDK Cookbook

经典的模式识别与机器学习教材的课后习题Pattern Recognition and Machine Learning习题答案