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Articles related to "common"


Inside China’s plan to lead the world in AI

  • Since the outbreak of COVID-19, medical AI applications have surged as Chinese researchers and tech companies have rushed to try and combat the virus by speeding up screening, diagnosis and new drug development.
  • But a closer look at the dynamics of China’s AI development reveals the importance of local government in implementing innovation policy.
  • Municipal and provincial governments across China are establishing cross-sector partnerships with research institutions and tech companies to create local AI innovation ecosystems and drive rapid research and development.
  • A promising example is the city of Hangzhou, in Zhejiang Province, which has established an “AI Town”, clustering together the tech company Alibaba, Zhejiang University and local businesses to work collaboratively on AI development.
  • China’s local ecosystem approach could offer interesting insights to policymakers in the UK aiming to boost research and innovation outside the capital and tackle longstanding regional economic imbalances.

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Bullet Point Approach to Data Imbalance!

  • Perhaps one of the most common ways of dealing with data imbalance is via over-sampling the minority class or under-sampling the majority class.
  • Common classification algorithms try to minimize the classification error (if you misclassify A as B, it will penalize the algorithm by increasing the cost function).
  • They punish the algorithm more when it misclassifies the minority class (the minority class gets a higher weight).
  • The method is mainly designed to address one and two-stage object recognition and is an upgrade on cross-entropy loss function by introducing a focusing factor.
  • The focus factor gives higher weight to the more “hard” examples.Read the paper by Facebook AI Research.
  • There is so much more but pragmatically these are the very useful techniques for dealing with unbalanced data.
  • I also want to point out that sometimes it’s just easier to re-frame the problem as an anomaly detection problem!

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Bullet Point Approach to Data Imbalance!

  • Perhaps one of the most common ways of dealing with data imbalance is via over-sampling the minority class or under-sampling the majority class.
  • Common classification algorithms try to minimize the classification error (if you misclassify A as B, it will penalize the algorithm by increasing the cost function).
  • They punish the algorithm more when it misclassifies the minority class (the minority class gets a higher weight).
  • The method is mainly designed to address one and two-stage object recognition and is an upgrade on cross-entropy loss function by introducing a focusing factor.
  • The focus factor gives higher weight to the more “hard” examples.Read the paper by Facebook AI Research.
  • There is so much more but pragmatically these are the very useful techniques for dealing with unbalanced data.
  • I also want to point out that sometimes it’s just easier to re-frame the problem as an anomaly detection problem!

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Trump suspended from Twitch, as Reddit bans the 'The_Donald' and additional subreddits

  • They’re sending people that have lots of problems, and they’re bringing those problems with us.
  • They’re sending us not the right people.
  • That news follows the recent ban of the massive The_Donald subreddit, which sported more than 790,000 users, largely devoted to sharing content about Trump.
  • Reddit confirmed the update to its policy that resulted in the ban, along with 2,000 other subreddits, including one devoted to the hugely popular leftist comedy podcast, Chapo Trap House.
  • All communities on Reddit must abide by our content policy in good faith.
  • Until now, we’ve worked in good faith to help them preserve the community as a space for its users—through warnings, mod changes, quarantining, and more.
  • After Twitter played whack-a-mole with problematic tweets around mail-in voting and other issues, he signed an executive order taking aim at Section 230 of the Communications Decency Act, which protects sites from being sued for content posted by users.

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Would You Rather be a Data Analyst or Data Scientist?

  • After working as both a professional data analyst and data scientist, I thought it would be insightful to highlight the experience of each position along with some key differences in how they feel day-to-day.
  • While there is some overlap between the two positions, which I have highlighted in another article (linked at the end of this article) that covers the differences and similarities between the skills of these two roles, I wanted to now take some time to go over how it feels to be a data analyst versus a data scientist.
  • As a data analyst and data scientist, you can expect to share common tools like Tableau, SQL, and even Python, but the experience from each role can prove to be vastly different.

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