擀面皮&烙面皮&普通面皮之实验报告 - 未名空间(mitbbs.com)

擀面皮&烙面皮&普通面皮之实验报告 - 未名空间(mitbbs.com):



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Data Scientist | Tapjoy Info

Data Scientist | Tapjoy Info:



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SQL for Beginners - SQL Tutorial for Beginners with Examples - Udemy

SQL for Beginners - SQL Tutorial for Beginners with Examples - Udemy:



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AA187 芝加哥险情_朱培刚_新浪博客

AA187 芝加哥险情_朱培刚_新浪博客:



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百度2014校园招聘深度学习算法研发工程师笔试题

百度2014校园招聘深度学习算法研发工程师笔试题: "百度2014校园招聘深度学习算法研发工程师笔试题"



http://www.360doc.com/content/14/0325/18/13256259_363658929.shtml



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如何做cc150算法题

Cracking the coding interview--问题与解答:



http://www.hawstein.com/posts/ctci-solutions-contents.html

高频题：

http://blog.csdn.net/sushiplus/article/details/17596305

解题思路和答案：

http://hawstein.com/posts/ctci-solutions-contents.html

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8个月从CS菜鸟到拿到Google Offer的经历+内推 - 靖空间 - 博客频道 - CSDN.NET

8个月从CS菜鸟到拿到Google Offer的经历+内推 - 靖空间 - 博客频道 - CSDN.NET: ". 越败越战
不断磨练自己的面试技巧
发现知识漏洞，及时补救
"



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[转载] Facebook,Google,Microsoft offer 及 面试经历 - 2013.03 | 阿蘑多

[转载] Facebook,Google,Microsoft offer 及 面试经历 - 2013.03 | 阿蘑多: "G电面先warm up 一题是一个array 变BST， 第二题是 skip list，这题以前出现过，
"



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kaldi上使用gpu以及如何安装cuda - u010384318的专栏 - 博客频道 - CSDN.NET

kaldi上使用gpu以及如何安装cuda - u010384318的专栏 - 博客频道 - CSDN.NET:



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66 job interview questions for data scientists - Data Science Central

66 job interview questions for data scientists - Data Science Central:



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Machine Learning Algorithm Cheat Sheet - Laura Diane Hamilton

Machine Learning Algorithm Cheat Sheet - Laura Diane Hamilton: "Algorithm Pros Cons Good at
Linear regression - Very fast (runs in constant time)
- Easy to understand the model
- Less prone to overfitting - Unable to model complex relationships
-Unable to capture nonlinear relationships without first transforming the inputs - The first look at a dataset
- Numerical data with lots of features
Decision trees - Fast
- Robust to noise and missing values
- Accurate - Complex trees are hard to interpret
- Duplication within the same sub-tree is possible - Star classification
- Medical diagnosis
- Credit risk analysis
Neural networks - Extremely powerful
- Can model even very complex relationships
- No need to understand the underlying data
– Almost works by “magic” - Prone to overfitting
- Long training time
- Requires significant computing power for large datasets
- Model is essentially unreadable - Images
- Video
- “Human-intelligence” type tasks like driving or flying
- Robotics
Support Vector Machines - Can model complex, nonlinear relationships
- Robust to noise (because they maximize margins) - Need to select a good kernel function
- Model parameters are difficult to interpret
- Sometimes numerical stability problems
- Requires significant memory and processing power - Classifying proteins
- Text classification
- Image classification
- Handwriting recognition
K-Nearest Neighbors - Simple
- Powerful
- No training involved (“lazy”)
- Naturally handles multiclass classification and regression - Expensive and slow to predict new instances
- Must define a meaningful distance function
- Performs poorly on high-dimensionality datasets - Low-dimensional datasets
- Computer security: intrusion detection
- Fault detection in semiconducter manufacturing
- Video content retrieval
- Gene expression
- Protein-protein interaction"



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Baidu’s Andrew Ng on Deep Learning and Innovation in Silicon Valley - Digits - WSJ

Baidu’s Andrew Ng on Deep Learning and Innovation in Silicon Valley - Digits - WSJ: "generous interpretation"



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(10) What are some fundamental deep learning papers for which code and data is available to reproduce the result and on the way grasp deep learning? - Quora

(10) What are some fundamental deep learning papers for which code and data is available to reproduce the result and on the way grasp deep learning? - Quora:



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UFLDL Exercise:Convolution and Pooling - YSM'wall - 博客频道 - CSDN.NET

UFLDL Exercise:Convolution and Pooling - YSM'wall - 博客频道 - CSDN.NET:

http://m.blog.csdn.net/blog/linger2012liu/


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Unsupervised Feature Learning and Deep Learning Tutorial

Unsupervised Feature Learning and Deep Learning Tutorial:



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DNN primer

http://ufldl.stanford.edu/wiki/index.php/UFLDL_Tutorial
http://www.cnblogs.com/tornadomeet/tag/Deep%20Learning/
http://deeplearning.net/tutorial/
http://info.usherbrooke.ca/hlarochelle/neural_networks/content.html