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For over 50 years, distributed systems experts have been working hard to achieve the vision of making many machines work harmoniously together as though they were one. In this blog post, Dr. Krishna Sridhar digs into the challenges and benefits of using distributed systems for modern day machine learning needs.
Deep neural networks that learn to represent data in multiple layers of increasing abstraction have dramatically improved the state-of-the-art for speech recognition, object recognition, object detection, predicting the activity of drug molecules, and many other tasks. Deep learning discovers intricate structure in large datasets by building distributed representations, either via supervised, unsupervised or reinforcement learning. The Deep Learning Summer School 2015 is aimed at graduate students and industrial engineers and researchers who already have some basic knowledge of machine learning (and possibly but not necessarily of deep learning) and wish to learn more about this rapidly growing field of research.
An attacker can draw attention to items that don't deserve that attention by manipulating recommender systems. We describe an influence-limiting algorithm that can turn existing recommender systems into manipulation-resistant systems.
Honest reporting is the optimal strategy for raters who wish to maximize their influence. If an attacker can create only a bounded number of shills, the attacker can mislead only a small amount. However, the system eventually makes full use of information from honest, informative raters. We describe both the influence limits and the information loss incurred due to those limits in terms of information-theoretic concepts of loss functions and entropies.
Turning something raw into something industrially valuable has always required 2 things; science and engineering. The science is our attempt to explain and predict the behavior exhibited by some complex system and capture those explanations in the form of testable models. The engineering looks to mechanize those modeled concepts into useable tools that make a direct impact on society. As we move into the information age our definition of what we consider valuable is shifting to something more in
A new survey of 490 data professionals from small to large companies, conducted by AnalyticsWeek in partnership with Business Over Broadway, provides a look into the field of data science. Download the free Executive Summary of the report, Optimizing your Data Science Teams.
Typically, user-user collaborative filtering has used Pearson correlation to compare users. Early work tried Spearman correlation and (raw) cosine similarity, but found Pearson to work better, and the issue wasn’t revisited for quite some time.
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