Tag Archives: research

Deep Learning Research Paper Lists for Summer 2017

The last links are not official academic papers, but they are quite good resources on deep learning.

5 Data Science Research Papers to read in Summer 2017

In the past, the blog has included 7 Important Data Science Papers and 5 More Data Science Papers. Here is another list if you are looking for something to read over the summer.

7 Important Data Science Papers

It is back-to-school time, and here are some papers to keep you busy this school year. All the papers are free. This list is far from exhaustive, but these are some important papers in data science and big data.

Google Search

  • PageRank – This is the paper that explains the algorithm behind Google search.

Hadoop

  • MapReduce – This paper explains a programming model for processing large datasets. In particular, it is the programming model used in hadoop.
  • Google File System – Part of hadoop is HDFS. HDFS is an open-source version of the distributed file system explained in this paper.

NoSQL

These are 2 of the papers that drove/started the NoSQL debate. Each paper describes a different type of storage system intended to be massively scabable.

Machine Learning

Bonus Paper

  • Random Forests – One of the most popular machine learning techniques. It is heavily used in Kaggle competitions, even by the winners.

Are there any other papers you feel should be on the list?

12 Useful Tips for Machine Learning

Pedro Domingos of the Department of Computer Science and Engineering at the University of Washington provides a very useful paper with tips for machine learning. The paper is title, A Few Useful Things to Know about Machine Learning [pdf].

Below are the 12 useful tips.

  1. LEARNING = REPRESENTATION + EVALUATION + OPTIMIZATION
  2. IT’S GENERALIZATION THAT COUNTS
  3. DATA ALONE IS NOT ENOUGH
  4. OVERFITTING HAS MANY FACES
  5. INTUITION FAILS IN HIGH DIMENSIONS
  6. THEORETICAL GUARANTEES ARE NOT WHAT THEY SEEM
  7. FEATURE ENGINEERING IS THE KEY
  8. MORE DATA BEATS A CLEVERER ALGORITHM
  9. LEARN MANY MODELS, NOT JUST ONE
  10. SIMPLICITY DOES NOT IMPLY ACCURACY
  11. REPRESENTABLE DOES NOT IMPLY LEARNABLE
  12. CORRELATION DOES NOT IMPLY CAUSATION

For details and a good explanation of each, see the paper A Few Useful Things to Know about Machine Learning [pdf].

Also,later this year, Pedro Domingos will be teaching a machine learning course via Coursera. Sign up if you are interested.