KDnuggets Home » News » 2017 » May » News, Features » Top Stories, May 8-14: Annual KDnuggets Data Science Software Poll; Using Deep Learning To Extract Knowledge From Job Descriptions ( 17:n19 )

Top Stories, May 8-14: Annual KDnuggets Data Science Software Poll; Using Deep Learning To Extract Knowledge From Job Descriptions


 
 
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New Poll: What software you used for Analytics, Data Mining, Data Science, Machine Learning projects in the past 12 months?; Using Deep Learning To Extract Knowledge From Job Descriptions; Deep Learning Past, Present, and Future; The Guerrilla Guide to Machine Learning with R


Most Popular Last Week

  1. new New Poll: What software you used for Analytics, Data Mining, Data Science, Machine Learning projects in the past 12 months?, by Gregory Piatetsky
  2. new Past, present & future Deep Learning – Past, Present, and Future, by Henry H. Eckerson
  3. decrease 10 Free Must-Read Books for Machine Learning and Data Science
  4. new Using Deep Learning To Extract Knowledge From Job Descriptions
  5. new How to Learn Machine Learning in 10 Days
  6. decrease The 10 Algorithms Machine Learning Engineers Need to Know
  7. new The Guerrilla Guide to Machine Learning with R

Most Shared Last Week

  1. Recruitment Using Deep Learning To Extract Knowledge From Job Descriptions, by Jan Luts, - May 09, 2017.
  2. The Guerrilla Guide to Machine Learning with R, by Matthew Mayo - May 11, 2017.
  3. Must-Know: How to determine the most useful number of clusters? - May 09, 2017.
  4. Data Version Control: iterative machine learning - May 11, 2017.
  5. Data Science & Machine Learning Platforms for the Enterprise - May 08, 2017.
  6. 5 Machine Learning Projects You Can No Longer Overlook, May - May 10, 2017.
  7. The Two Phases of Gradient Descent in Deep Learning - May 12, 2017.

Most Popular Past 30 Days

  1. decrease 10 Free Must-Read Books for Machine Learning and Data Science
  2. decrease The 10 Algorithms Machine Learning Engineers Need to Know
  3. new How to Learn Machine Learning in 10 Days
  4. increase Forrester vs Gartner on Data Science Platforms and Machine Learning Solutions
  5. new Keep it simple! How to understand Gradient Descent algorithm
  6. new Awesome Deep Learning: Most Cited Deep Learning Papers
  7. new New Poll: What software you used for Analytics, Data Mining, Data Science, Machine Learning projects in the past 12 months?

Most Shared Past 30 Days

  1. New Poll: What software you used for Analytics, Data Mining, Data Science, Machine Learning projects in the past 12 months? - May 05, 2017.
  2. Deep Learning – Past, Present, and Future - May 02, 2017.
  3. How to Learn Machine Learning in 10 Days - May 01, 2017.
  4. Awesome Deep Learning: Most Cited Deep Learning Papers - Apr 21, 2017.
  5. Using Deep Learning To Extract Knowledge From Job Descriptions - May 09, 2017.
  6. Keep it simple! How to understand Gradient Descent algorithm - Apr 28, 2017.
  7. Data Science for the Layman (No Math Added) - Apr 20, 2017.



Top Stories Past 30 Days

Most Popular
  1. 30 Essential Data Science, Machine Learning & Deep Learning Cheat Sheets
  2. Understanding Machine Learning Algorithms
  3. How To Become a 10x Data Scientist, part 1
  4. Introduction to Blockchains & What It Means to Big Data
  5. Python overtakes R, becomes the leader in Data Science, Machine Learning platforms
  6. The 10 Algorithms Machine Learning Engineers Need to Know
  7. Data Science and the Imposter Syndrome
Most Shared
  1. Understanding Machine Learning Algorithms
  2. Introduction to Blockchains & What It Means to Big Data
  3. Top 10 Active Big Data, Data Science, Machine Learning Influencers on LinkedIn, Updated
  4. Using Machine Learning to Predict and Explain Employee Attrition
  5. Keras Cheat Sheet: Deep Learning in Python
  6. XGBoost, a Top Machine Learning Method on Kaggle, Explained
  7. Want to Become a Data Scientist? Read This Interview First

KDnuggets Home » News » 2017 » May » News, Features » Top Stories, May 8-14: Annual KDnuggets Data Science Software Poll; Using Deep Learning To Extract Knowledge From Job Descriptions ( 17:n19 )