grzegorzgwardys.wordpress.com
April 2016 – Grzegorz Gwardys
https://grzegorzgwardys.wordpress.com/2016/04
Experience with Machine/Deep Learning and more …. Convolutional Neural Networks backpropagation: from intuition to derivation. April 22, 2016. January 14, 2017. Disclaimer: It is assumed that the reader is familiar with terms such as Multilayer Perceptron, delta errors or backpropagation. If not, it is recommended to read for example a chapter 2. Of free online book ‘Neural Networks and Deep Learning’ by Michael Nielsen. By Quora, Facebook or G . It was a little consoling, when I found out that I am not ...
grzegorzgwardys.wordpress.com
Convolutional Neural Networks backpropagation: from intuition to derivation – Grzegorz Gwardys
https://grzegorzgwardys.wordpress.com/2016/04/22/8
Experience with Machine/Deep Learning and more …. Convolutional Neural Networks backpropagation: from intuition to derivation. April 22, 2016. January 14, 2017. Disclaimer: It is assumed that the reader is familiar with terms such as Multilayer Perceptron, delta errors or backpropagation. If not, it is recommended to read for example a chapter 2. Of free online book ‘Neural Networks and Deep Learning’ by Michael Nielsen. By Quora, Facebook or G . It was a little consoling, when I found out that I am not ...
yerevann.github.io
Implementing Dynamic memory networks · YerevaNN
http://yerevann.github.io/2016/02/05/implementing-dynamic-memory-networks
Blog on neural networks. Implementing Dynamic memory networks. The Allen Institute for Artificial Intelligence has organized a 4 month contest. In Kaggle on question answering. The aim is to create a system which can correctly answer the questions from the 8th grade science exams of US schools (biology, chemistry, physics etc.). DeepHack Lab organized a scientific school hackathon. By Facebook researchers Weston, Bordes, Chopra, Rush, Merriënboer and Mikolov, where the authors introduce a benchmark of to...
rt.dgyblog.com
Random Thoughts : Learning Deep Learning
http://rt.dgyblog.com/ref/ref-learning-deep-learning.html
A wiki of my random thoughts. Multi Layer Perceptron Layers. A Story of My OS. There are lots of awesome reading lists or posts that summarized materials related to Deep Learning. So why would I commit another one? By Erik Demaine and Srinivas Devadas. By Arthur Mattuck, Haynes Miller, Jeremy Orloff, John Lewis. Theory of Computation, Learning Theory, Neuroscience, etc. Introduction to the Theory of Computation. Artificial Intelligence: A Modern Approach. By Stuart Russell and Peter Norvig. By Richard S&...
52nlp.cn
我爱自然语言处理 | I Love Natural Language Processing | 第 2 页
http://www.52nlp.cn/page/2
I Love Natural Language Processing. 并将决定Io epoll thread和app epoll thread的负载均衡 桂洪冠。 假设 三个proxy server的属于同一epoll thread，且三个proxy server假设都处理能力无限大。 限制 如果刚开始时待处理队列的数据包个数为100个，多次发送轮回后proxy server A proxy server B proxy server C， 每个发送的最多发送协议包数为待处理队列协议包个数 * 该连接所占权重. 当App epoll thread将协议包从待处理队列中移除时，会将该协议包在客户端的处理时间、该协议包的超时时间、该协议包的proxy server接收时间戳、当前时间戳来判断该协议包是否已超时。 Proxy client的io epoll thread通过检测对端DPIO连接的可写事件，从发送队列中获取请求包，将api的index加入到协议包的api index字段。 Proxy client的io epoll thread从共享内存中读取协议包，释放由请求包中所标识的内存空间。
thegrandjanitor.com
The Grand Janitor Blog V2 | Speech Recognition, Machine Learning, and Random Musing of Arthur Chan | Page 2
http://thegrandjanitor.com/page/2
The Grand Janitor Blog V2. My Machine Learning Portfolio. Using ARPA LM with Python. December 28, 2015. During Christmas, I tried to do some small fun hack with language modeling. That obviously requires. An LM There are many ways to do so. But here is new method which I really like: use the python interface of KenLM. So here is a note for myself (largely adapted from Victor Chahuneau):. Install boost-1.6.0. First, install libbz2:. Sudo apt-get install libbz2-dev. Then install 1.6.0: download here. When ...
iphone3310.wordpress.com
iphone3310 – 2 頁
https://iphone3310.wordpress.com/page/2
Paper Summary]A Bayesian Hierarchical Model for Learning Natural Scene Categories. A Bayesian Hierarchical Model for Learning Natural Scene Categories, Li Fei-Fei, Pietro Perona. This work extends BOW with Bayesian hierarchical models, an “advanced PLSA" approach:. Above chart briefly shows how the algorithm works, but to go deeper, we need to dig inside and answer:How do images “chosen" by a model? Consequently, we have to determine “a distribution of topics" from “many distributions of topi...This proc...
socher.org
Richard Socher - Deep Learning Tutorial
http://www.socher.org/index.php/DeepLearningTutorial/DeepLearningTutorial
Back to Main Site. ACL 2012 NAACL 2013 Tutorial: Deep Learning for NLP (without Magic). Richard Socher, Chris Manning. In the spring quarter of 2015, I gave an entire class at Stanford on deep learning for natural language processing. If you're interested in all the details of these methods and applications, see http:/ cs224d.stanford.edu. Http:/ lxmls.it.pt/2014/socher-lxmls.pdf. Most recent version from a talk at the Machine Learning Summer School in Lisbon 2014. 25MB) - 184 slides. Tricks of the trade.
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