As Machine Learning practitioners, when faced with a task, we usually select or train a model primarily based on how well it performs on that task. 10min read.
continue with the part 1.
This article explains the fundamentals of how a computer can learn to infer the polarity of a given document and use it as an excuse to introduce different concepts used in NLP. by Enrique Fueyo, 10 min read. Continue reading “Understanding Sentiment Analysis – Part 1”
In partnership with Google, we today have open sourced Kayenta, a platform for automated canary analysis, a crucial component of delivery to reduce the risk from making changes in production environment. by Michael Graff and Chris Sanden. 8 mins read.
Take closer look at inverse reinforcement learning (IRL) which is the field of learning an agent’s objectives, values, or rewards by observing its behavior. by Johannes Heidecke. 15 min read.
Understand the difference between the two main types of machine learning methods. by Devin Soni. 5 mins read.
The author explained how various facts (portfolio, kaggle competition, github, blogging, etc..) play important role to land the first data science job. by Reshama Shailkh, 4 min read. Continue reading “How Do I Get My First Data Science Job?”
we introduce some of the challenges in the content-delivery space where our data science and engineering teams collaborate to optimize the Netflix service. by Andrew Berglund, 7 mins read
practical aspects of what’s behind the scenes of data science and analytics at LinkedIn. and what we look for when interview a data scientist candidates. by michael li. 5 min read. Continue reading “Speaker Post: Skills that LinkedIn looks for when hiring data scientist”
A comprehensive tech review on how deep learning is applied to object detection, and how these object detection models both inspire and diverge from one another. by Joyce Xu. 10 min read.