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Top Stories, Jun 12-18: Top 15 Python Libraries for Data Science in 2017; Deep Learning Papers Reading Roadmap


Top 15 Python Libraries for Data Science in 2017; Deep Learning Papers Reading Roadmap; The Practical Importance of Feature Selection; Understanding Deep Learning Requires Re-thinking Generalization; K-means Clustering with Tableau



Most Popular Last Week

  1. new Matplotlib commits Top 15 Python Libraries for Data Science in 2017, by Igor Bobriakov
  2. new 6 Interesting Things You Can Do with Python on Facebook Data, by Nour Galaby
  3. new Deep Learning Papers Reading Roadmap
  4. decrease The 10 Algorithms Machine Learning Engineers Need to Know
  5. new Data Scientist: Learn the Skills you need for free
  6. increase 10 Free Must-Read Books for Machine Learning and Data Science
  7. new Is Regression Analysis Really Machine Learning?

Most Shared Last Week

  1. Deep Learning Roadmap Deep Learning Papers Reading Roadmap, by Flood Sung - Jun 13, 2017.
  2. Top 15 Python Libraries for Data Science in 2017, by Igor Bobriakov - Jun 13, 2017.
  3. The Practical Importance of Feature Selection - Jun 12, 2017.
  4. Understanding Deep Learning Requires Re-thinking Generalization - Jun 16, 2017.
  5. K-means Clustering with Tableau – Call Detail Records Example - Jun 16, 2017.
  6. 7 Ways to Get High-Quality Labeled Training Data at Low Cost - Jun 13, 2017.
  7. Medical Image Analysis with Deep Learning, Part 3 - Jun 15, 2017.

Most Popular Past 30 Days

  1. new Machine Learning Workflows in Python from Scratch Part 1: Data Preparation
  2. increase The 10 Algorithms Machine Learning Engineers Need to Know
  3. new Top 15 Python Libraries for Data Science in 2017
  4. increase 10 Free Must-Read Books for Machine Learning and Data Science
  5. new New Leader, Trends, and Surprises in Analytics, Data Science, Machine Learning Software Poll
  6. new 6 Interesting Things You Can Do with Python on Facebook Data
  7. new 7 Steps to Mastering Data Preparation with Python

Most Shared Past 30 Days

  1. New Leader, Trends, and Surprises in Analytics, Data Science, Machine Learning Software Poll - May 22, 2017.
  2. Which Machine Learning Algorithm Should I Use? - Jun 01, 2017.
  3. 7 Steps to Mastering Data Preparation with Python - Jun 02, 2017.
  4. Must-Know: What are common data quality issues for Big Data and how to handle them? - May 16, 2017.
  5. Machine Learning Workflows in Python from Scratch Part 1: Data Preparation - May 29, 2017.
  6. Deep Learning Papers Reading Roadmap - Jun 13, 2017.
  7. Top 15 Python Libraries for Data Science in 2017 - Jun 13, 2017.