About Anmol Rajpurohit

Anmol Rajpurohit is a software engineer at Salesforce. His areas of interest are data science, machine learning and information retrieval. His novel analytics solution for online education was the runner-up at UCLA Developer's Contest 2014. His strong technical background and analytical skills have led him to a number of international research projects and internships. Besides research and development, he loves blogging on data science news and trends at kdnuggets.com. Anmol has BA in CS from LNM Institute for Information Technology (LNMIIT), India and MS in CS from UC Irvine, USA.

Anmol Rajpurohit Posts (324)

• Key Takeaways from AI Conference SF, Day 2: AI and Security, Adversarial Examples, Innovation - 30 Oct 2018
  
  Highlights and key takeaways from selected keynote sessions on day 2 of AI Conference San Francisco 2018.
• Key Takeaways from AI Conference SF, Day 1: Domain Specific Architectures, Emerging China, AI Risks - 29 Oct 2018
  
  Highlights and key takeaways include Domain Specific Architectures – the next big thing, Emerging China – evolving from copying ideas to true innovation, and Addressing Risks in AI – Security, Privacy, and Ethics.
• Key Takeaways from KDD 2018: a Deconfounder, Machine Learning at Pinterest, Knowledge Graph - 11 Sep 2018
  
  Highlights and key takeaways from KDD 2018, 24th ACM SIGKDD conference on Data Science and Data Mining: including what is a deconfounder, how Pinterest approaches Machine Learning, Knowledge Graph for Products, and Differential Privacy.
• Key Takeaways from AI Conference in San Francisco 2017 – Day 2 - 02 Oct 2017
  
  Highlights and key takeaways from day 2 of AI Conference San Francisco 2017, including current state review, future trends, and top recommendations for AI initiatives.
• Key Takeaways from AI Conference in San Francisco 2017 – Day 1 - 29 Sep 2017
  
  Highlights and key takeaways from day 1 of AI Conference San Francisco 2017, including current state review, future trends, and top recommendations for AI initiatives.
• Must-Know: What are common data quality issues for Big Data and how to handle them? - 16 May 2017
  
  Let's have a look at common quality issues facing Big Data in terms of the key characteristics of Big Data – Volume, Velocity, Variety, Veracity, and Value.
• Must-Know: When can parallelism make your algorithms run faster? When could it make your algorithms run slower? - 25 Apr 2017
  
  Efficient implementation is key to achieving the benefits of parallelization, even though parallelism is a good idea when the task can be divided into sub-tasks that can be executed independent of each other without communication or shared resources.
• Must-Know: Why it may be better to have fewer predictors in Machine Learning models? - 04 Apr 2017
  
  There are a few reasons why it might be a better idea to have fewer predictor variables rather than having many of them. Read on to find out more.
• Key Takeaways from Strata + Hadoop World 2017 San Jose, Day 2 - 29 Mar 2017
  
  The focus is increasingly shifting from storing and processing Big Data in an efficient way, to applying traditional and new machine learning techniques to drive higher value from the data at hand.
• Key Takeaways from Strata + Hadoop World 2017 San Jose, Day 1 - 24 Mar 2017
  
  The focus is increasingly shifting from storing and processing Big Data in an efficient way, to applying traditional and new machine learning techniques to drive higher value from the data at hand.