2018 Aug Opinions, Interviews
All (100) | Courses, Education (3) | Meetings (13) | News, Features (11) | Opinions, Interviews (15) | Top Stories, Tweets (9) | Tutorials, Overviews (43) | Webcasts & Webinars (6)
- Three Ways Big Data and Machine Learning Reinvent Online Video Experience - Aug 31, 2018.
With traditional TV viewing on the decline, we discuss several ways Big Data and Machine Learning can assist with online video, including redefining user recommendations, improving video buffering and leveraging MAM orchestration.
- Self-Service Data Prep Tools vs Enterprise-Level Solutions? 6 Lessons Learned - Aug 30, 2018.
A detailed comparison between self-service data preparation tools and enterprise-level solutions, covering business strategy, accessible tools and solutions and more.
- Skip the Interview! 9 Benefits of Career Fairs - Aug 29, 2018.
Career fairs are a great way to get your feet wet if you’re just starting your data science career, or to be exposed to newer trends and emerging organizations if you’re already established. What other ways are career fairs beneficial?
- What Data Scientists Want? - Aug 27, 2018.
We examine what's important for data scientists in their careers, including challenging work, networking with peers, foreseeing their career path and creating a good work-life balance.
- DynamoDB vs. Cassandra: from “no idea” to “it’s a no-brainer” - Aug 23, 2018.
DynamoDB vs. Cassandra: have they got anything in common? If yes, what? If no, what are the differences? We answer these questions and examine performance of both databases.
- Leveraging Agent-based Models (ABM) and Digital Twins to Prevent Injuries - Aug 22, 2018.
Both athletes and machines deal with inter-twined complex systems (where the interactions of one complex system can have a ripple effect on others) that can have significant impact on their operational effectiveness.
- Cartoon: Machine Learning takes a vacation - Aug 18, 2018.
August is a popular time for vacation, and even hard-working AI may want to take a few epochs off from its training. KDnuggets Cartoon looks at how this might go.
- Project Hydrogen, new initiative based on Apache Spark to support AI and Data Science - Aug 16, 2018.
An introduction to Project Hydrogen: how it can assist machine learning and AI frameworks on Apache Spark and what distinguishes it from other open source projects.
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Data Scientist guide for getting started with Docker - Aug 14, 2018.
Docker is an increasingly popular way to create and deploy applications through virtualization, but can it be useful for data scientists? This guide should help you quickly get started. - Affordable online news archives for academic research - Aug 10, 2018.
Many researchers need access to multi-year historical repositories of online news articles. We identified three companies that make such access affordable, and spoke with their CEOs.
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Top 10 roles in AI and data science - Aug 9, 2018.
When you think of the perfect data science team, are you imagining 10 copies of the same professor of computer science and statistics, hands delicately stained with whiteboard marker? We hope not! - How GOAT Taught a Machine to Love Sneakers - Aug 7, 2018.
Embeddings are a fantastic tool to create reusable value with inherent properties similar to how humans interpret objects. GOAT uses deep learning to generate these for their entire sneaker catalogue.
- Seven Practical Ideas For Beginner Data Scientists - Aug 7, 2018.
As someone who has been there, I’d like to outline a few practical ideas to help junior data scientists get started at a small software company. The steps were drawn from my personal journey and that of others before me.
- Eight iconic examples of data visualisation - Aug 3, 2018.
A collection of the most exemplary examples of data visualizations, including Napoleons invasion of Russia and the iconic London Underground map.
- Data Scientist Interviews Demystified - Aug 2, 2018.
We look at typical questions in a data science interview, examine the rationale for such questions, and hope to demystify the interview process for recent graduates and aspiring data scientists.
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