Mar 6, 2019
Finally done with Brandon Foltz videos on logistic regression.
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Finally done with Brandon Foltz videos on logistic regression.
Required share fom The Morning Paper: Snorkel: rapid training data creation with weak supervision — the morning paper Snorkel: rapid training data creation with weak supervision Ratner et al., VLDB’18 Earlier this week we looked at Sparser, which comes from the Stanford Dawn project, “a five-year research project to democratize AI by making it dramatically easier to build AI-powered applications.” Today’s paper choice, Snorkel, is from the same stable. It tackles one of […] via Snorkel: rapid training data creation with weak supervision — the morning paper
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Stochastic gradient descent activity noted in Xi' an's Og
Stochastic gradient descent activity noted in Xi’ an’s Og
The reader of this blog is now acquainted with some blogs that I follow and sometimes repost here. One of these is from a French Statistician and Data Science that posts regularly in the unusually named blog Xi’ an’s OG. Here it is a brief biographical description of the author of this Blog, which sports a somewhat mysterious identity style of Blogging. Something that pays some tribute to…
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Elements Of Statistical Learning
Elements Of Statistical Learning
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Elements of Statistical Learning: data mining, inference and prediction (2nd Edition) (with J. Friedman, Springer-Verlag, 2009).(More…)
This new two-day course gives a detailed and modern overview of statistical models used by data scientists for prediction and inference, including sparse models and deep learning.(More…)
It focuses on formulation of prediction…
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I’ve learned many things since I joined Civis. Least expected though is a new appreciation for simple linear regression and classification models. Shortly
What is Data Science?
Data Science is an interdisciplinary field about processes and systems to extract knowledge or insights from data in various forms, either structured or unstructured, which is a continuation of some of the data analysis fields such as statistics, data mining, and predictive analytics.
Data Science employs techniques and theories drawn from many fields within the broad areas of mathematics, statistics, operations research,information science, and computer science, including signal processing, probability models, machine learning, statistical learning, data mining,database, data engineering, pattern recognition and learning, visualization, predictive analytics, uncertainty modeling, data warehousing, data compression, computer programming, artificial intelligence, and high performance computing. Methods that scale to big data are of particular interest in data science, although the discipline is not generally considered to be restricted to such big data, and big data technologies are often focused on organizing and preprocessing the data instead of analysis. The development of machine learning has enhanced the growth and importance of data science.