Topics: AI | Data Science | Data Visualization | Deep Learning | Machine Learning | NLP | Python | R | Statistics

About Zachary Chase Lipton

Zachary Chase Lipton is a PhD student in the Computer Science Engineering department at the University of California, San Diego. Funded by the Division of Biomedical Informatics, he is interested in both theoretical foundations and applications of machine learning. In addition to his work at UCSD, he has interned at Microsoft Research Labs. He also blogs at Approximately Correct.

Zachary Chase Lipton Posts (31)

  • Rich Data Summit Takeaways - 19 Oct 2015
    Data scientists get excited about algorithms. But nearly all time spent working with data involves acquiring, pipelining, annotating and cleaning it. At the Rich Data Summit in SF, data's dirty work took center stage.
  • Does Deep Learning Come from the Devil? - 09 Oct 2015
    Deep learning has revolutionized computer vision and natural language processing. Yet the mathematics explaining its success remains elusive. At the Yandex conference on machine learning prospects and applications, Vladimir Vapnik offered a critical perspective.
  • Recycling Deep Learning Models with Transfer Learning - 14 Aug 2015
    Deep learning exploits gigantic datasets to produce powerful models. But what can we do when our datasets are comparatively small? Transfer learning by fine-tuning deep nets offers a way to leverage existing datasets to perform well on new tasks.
  • and the 24 Hour Research Cycle - 21 Jul 2015 gives researchers the ability to instantly publish research, free of peer review and the publication cycle. This capability offers both advantages and pitfalls. We should warily eye the 24-7 news cycle as a cautionary tale for how this could go wrong.
  • Deep Learning and the Triumph of Empiricism - 07 Jul 2015
    Theoretical guarantees are clearly desirable. And yet many of today's best-performing supervised learning algorithms offer none. What explains the gap between theoretical soundness and empirical success?
  • Not So Fast: Questioning Deep Learning IQ Results - 15 Jun 2015
    Did deep learning just leap towards human intelligence? Not so fast.
  • Will the Real Data Scientists Please Stand Up? - 18 May 2015
    Job postings for data scientists are everywhere. But what is a data scientist? I present a few archetypes.
  • Cloud Machine Learning’s Ostrich Mania & Uncanny Valley - 14 May 2015
    Cloud machine learning services are popping up by the tens, providing automated data science solutions. What will the anticipated customers want? They may follow a peculiar distribution reminiscent of the uncanny valley.
  • The Myth of Model Interpretability - 27 Apr 2015
    Deep networks are widely regarded as black boxes. But are they truly uninterpretable in any way that logistic regression is not?
  • Cloud Machine Learning Wars: Amazon vs IBM Watson vs Microsoft Azure - 16 Apr 2015
    Amazon recently announced Amazon Machine Learning, a cloud machine learning solution for Amazon Web Services. Able to pull data effortlessly from RDS, S3 and Redshift, the product could pose a significant threat to Microsoft Azure ML and IBM Watson Analytics.

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