最近出來實習，淚奔，沒時間學習了，把一些覺得很好但是沒時間看的資源放這 以後學習
如果說理解一個技術的最高境界，就是能夠用最簡單的方式將這個技術表達出來的話，那麼Igor對於CPU Cache的理解絕對達到了此境界。他的博文：Gallery of Processor Cache Effects http://t.cn/hrXwvb 7個簡單至極的代碼示例，覆蓋了Cache Line、Cache Size、False Sharing等重要知識點，不得不服
NAACL今天的tutorial包括了斯坦福Richard Socher和Christopher Manning關於深度學習在NLP中應用的教學講座。看了一下slides，比去年ACL的版本增加了一些新內容，可以算是關於深度學習在語言技術的應用中相當全面的tutorial了。"Deep Learning for NLP (without Magic)" slides: http://t.cn/zHHyKUo
教程tutorial
ubc 的machine learning 2013 課程
有mcmc 以及最新的深度學習的課程
http://www.cs.ubc.ca/~nando/540-2013/lectures.html
文本挖掘技術
http://www.icst.pku.edu.cn/course/mining/11-12spring/index.html
rbm java 代碼 估計是最對我胃口的代碼
https://github.com/tjake/rbm-dbn-mnist
Stanford NLP組專門設置了Deep Learning in Natural Language Processing的主頁
http://nlp.stanford.edu/projects/DeepLearningInNaturalLanguageProcessing.shtml
一個大牛的主頁
這是其教學 有很多資料
http://alex.smola.org/teaching/
http://www.cs.princeton.edu/courses/archive/spring10/cos424/w/syllabus
The Large Scale Learning class notes
http://cilvr.cs.nyu.edu/doku.php?id=courses:bigdata:slides:start算法tutorial
一個劍橋大學教授的主頁 高斯過程的pdf講得很細很好
http://mlg.eng.cam.ac.uk/zoubin/
變分貝葉斯 tutorial 很nice
http://people.inf.ethz.ch/bkay/talks/Brodersen_2013_03_22.pdf
關於協同過濾 和graph mind 的hadoop 實現
https://code.google.com/p/hadoop-network/
單機模式處理大數據，蒐集一些好用的開源利器
1. LibFM
2. Svdfeature
項目主頁：http://apex.sjtu.edu.cn/apex_wiki/svdfeature
3. Libsvm和Liblinear
libsvm項目主頁：http://www.csie.ntu.edu.tw/~cjlin/libsvm/
liblinear項目主頁：http://www.csie.ntu.edu.tw/~cjlin/liblinear/
初次使用必讀：practical guide
libsvm的開發心得by林智仁：http://www.csie.ntu.edu.tw/~cjlin/talks/kdd.pdf
4. rt-rank
項目主頁：http://research.engineering.wustl.edu/~amohan/
rt-rank中實現了推薦系統中常見的random forests和gradient boosted decision trees這兩種方法，使用起來很方便。
3. Mahout
項目主頁：http://mahout.apache.org/
4. MyMediaLite
項目主頁：http://www.ismll.uni-hildesheim.de/mymedialite/
4. GraphLab 和 GraphChi
GraphLab項目主頁：http://graphlab.org/
GraphChi項目主頁：http://graphlab.org/graphchi/
GraphChi的下載地址：https://code.google.com/p/graphchi/downloads/detail?name=graphchi_src_v0.1.2_toolkits.tar.gz
CF for GraphChi: http://bickson.blogspot.com/2012/08/collaborative-filtering-with-graphchi.html
pylearn2
https://github.com/lisa-lab/pylearn2
包含很多特性 ，更新很快
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- Training algorithms
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A “default training algorithm” that asks the model to train itself
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- Stochastic gradient descent, with extensions including
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- Learning rate decay
- Momentum
- Polyak averaging
- Early stopping
- A simple framework for adding your own extensions
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Batch gradient descent with line searches
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Nonlinear conjugate gradient descent (with line searches)
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- Model Estimation Criteria
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- Score Matching
- Denoising Score Matching
- Noise-Contrastive Estimation
- Cross-entropy
- Log-likelihood
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- Models
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Autoencoders, including Contractive and Denoising Autoencoders
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- RBMs, including gaussian and ssRBM. Varying levels of integration into
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the full framework.
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k-means
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Local Coordinate Coding
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Maxout networks
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PCA
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Spike-and-Slab Sparse coding
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- SVMs (we provide a wrapper around scikit-learn that makes it easy to
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train a multiclass svm on dense training data in a memory efficient way, which doesn’t always happen using scikit-learn directly)
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- Partial implementation of DBMs (contact Ian Goodfellow if you would like
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to complete it)
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- Datasets:
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- MNIST, MNIST with background and rotations
- STL-10
- CIFAR-10, CIFAR-100
- NIPS Workshops 2011 Transfer Learning Challenge
- UTLC
- NORB
- Toronto Faces Dataset
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- Dataset pre-processing
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- Contrast normalization
- ZCA whitening
- Patch extraction (for implementing convolution-like algorithms)
- The Coates+Lee+Ng CIFAR processing pipeline
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- Miscellaneous algorithms and utilities:
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AIS
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Weight visualization for single layer networks
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- Can plot learning curves showing how user-configured quantities
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change during learning
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