Fisheye
http://bost.ocks.org/mike/fisheye/
D3.js (Data Driven Document)
http://d3js.org/
http://bost.ocks.org/mike/fisheye/
D3.js (Data Driven Document)
http://d3js.org/
gnuplotでeps
12 年前
2014年12月13日土曜日
2014年12月5日金曜日
2014年12月3日水曜日
Deep Learning
Application to Natural Language Processing : http://www.socher.org/
Distributed Neural Networks with GPUs in the AWS Cloud
http://techblog.netflix.com/2014/02/distributed-neural-networks-with-gpus.html
Large Scale Distributed Deep Networks
http://static.googleusercontent.com/media/research.google.com/en//archive/large_deep_networks_nips2012.pdf
Open Software
Deep Learning for Java
http://deeplearning4j.org/
Deeplearning4j is the first commercial-grade, open-source deep-learning library written in Java. It is meant to be used in business environments, rather than as a research tool for extensive data exploration. Deeplearning4j is most helpful in solving distinct problems, like identifying faces, voices, spam or e-commerce fraud. Deeplearning4j integrates with GPUs and includes a versatile n-dimensional array class. DL4J aims to be cutting-edge plug and play, more convention than configuration. By following its conventions, you get an infinitely scalable deep-learning architecture suitable for Hadoop and other big-data structures. This Java deep-learning library has a domain-specific language for neural networks that serves to turn their multiple knobs. Deeplearning4j includes a distributed deep-learning framework and a normal deep-learning framework (i.e. it runs on a single thread as well). Training takes place in the cluster, which means it can process massive amounts of data. Nets are trained in parallel via iterative reduce, and they are equally compatible with Java, Scala and Clojure, since they’re written for the JVM. This open-source, distributed deep-learning framework is made for data input and neural net training at scale, and its output should be highly accurate predictive models. By following the links at the bottom of each page, you will learn to set up, and train with sample data, several types of deep-learning networks. These include single- and multithread networks, Restricted Boltzmann machines, deep-belief networks, Deep Autoencoders, Recursive Neural Tensor Networks, Convolutional Nets and Stacked Denoising Autoencoders.
Distributed Neural Networks with GPUs in the AWS Cloud
http://techblog.netflix.com/2014/02/distributed-neural-networks-with-gpus.html
Large Scale Distributed Deep Networks
http://static.googleusercontent.com/media/research.google.com/en//archive/large_deep_networks_nips2012.pdf
Open Software
Deep Learning for Java
http://deeplearning4j.org/
Deeplearning4j is the first commercial-grade, open-source deep-learning library written in Java. It is meant to be used in business environments, rather than as a research tool for extensive data exploration. Deeplearning4j is most helpful in solving distinct problems, like identifying faces, voices, spam or e-commerce fraud. Deeplearning4j integrates with GPUs and includes a versatile n-dimensional array class. DL4J aims to be cutting-edge plug and play, more convention than configuration. By following its conventions, you get an infinitely scalable deep-learning architecture suitable for Hadoop and other big-data structures. This Java deep-learning library has a domain-specific language for neural networks that serves to turn their multiple knobs. Deeplearning4j includes a distributed deep-learning framework and a normal deep-learning framework (i.e. it runs on a single thread as well). Training takes place in the cluster, which means it can process massive amounts of data. Nets are trained in parallel via iterative reduce, and they are equally compatible with Java, Scala and Clojure, since they’re written for the JVM. This open-source, distributed deep-learning framework is made for data input and neural net training at scale, and its output should be highly accurate predictive models. By following the links at the bottom of each page, you will learn to set up, and train with sample data, several types of deep-learning networks. These include single- and multithread networks, Restricted Boltzmann machines, deep-belief networks, Deep Autoencoders, Recursive Neural Tensor Networks, Convolutional Nets and Stacked Denoising Autoencoders.
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