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The Many Faces of Exponential Weights in Online Learning

arXiv.org Machine Learning

A standard introduction to online learning might place Online Gradient Descent at its center and then proceed to develop generalizations and extensions like Online Mirror Descent and second-order methods. Here we explore the alternative approach of putting exponential weights (EW) first. We show that many standard methods and their regret bounds then follow as a special case by plugging in suitable surrogate losses and playing the EW posterior mean. For instance, we easily recover Online Gradient Descent by using EW with a Gaussian prior on linearized losses, and, more generally, all instances of Online Mirror Descent based on regular Bregman divergences also correspond to EW with a prior that depends on the mirror map. Furthermore, appropriate quadratic surrogate losses naturally give rise to Online Gradient Descent for strongly convex losses and to Online Newton Step. We further interpret several recent adaptive methods (iProd, Squint, and a variation of Coin Betting for experts) as a series of closely related reductions to exp-concave surrogate losses that are then handled by Exponential Weights. Finally, a benefit of our EW interpretation is that it opens up the possibility of sampling from the EW posterior distribution instead of playing the mean. As already observed by Bubeck and Eldan, this recovers the best-known rate in Online Bandit Linear Optimization.


Analysis of Dropout in Online Learning

arXiv.org Machine Learning

Deep learning is the state-of-the-art in fields such as visual object recognition and speech recognition. This learning uses a large number of layers and a huge number of units and connections. Therefore, overfitting is a serious problem with it, and the dropout which is a kind of regularization tool is used. However, in online learning, the effect of dropout is not well known. This paper presents our investigation on the effect of dropout in online learning. We analyzed the effect of dropout on convergence speed near the singular point. Our results indicated that dropout is effective in online learning. Dropout tends to avoid the singular point for convergence speed near that point.


Machine Learning with R Programming - Udemy

@machinelearnbot

This course contains lectures as videos along with the hands-on implementation of the concepts, additional assignments are also provided in the last section for your self-practice, working files are provided along with the first lecture. This course contains lectures as videos along with the hands-on implementation of the concepts, additional assignments are also provided in the last section for your self-practice, working files are provided along with the first lecture.



Online Learning for Changing Environments using Coin Betting

arXiv.org Machine Learning

A key challenge in online learning is that classical algorithms can be slow to adapt to changing environments. Recent studies have proposed "meta" algorithms that convert any online learning algorithm to one that is adaptive to changing environments, where the adaptivity is analyzed in a quantity called the strongly-adaptive regret. This paper describes a new meta algorithm that has a strongly-adaptive regret bound that is a factor of $\sqrt{\log(T)}$ better than other algorithms with the same time complexity, where $T$ is the time horizon. We also extend our algorithm to achieve a first-order (i.e., dependent on the observed losses) strongly-adaptive regret bound for the first time, to our knowledge. At its heart is a new parameter-free algorithm for the learning with expert advice (LEA) problem in which experts sometimes do not output advice for consecutive time steps (i.e., \emph{sleeping} experts). This algorithm is derived by a reduction from optimal algorithms for the so-called coin betting problem. Empirical results show that our algorithm outperforms state-of-the-art methods in both learning with expert advice and metric learning scenarios.


Arduino Robotics, IOT, Gaming for kids, Parents & Beginners

@machinelearnbot

Be a Technology Creator Today!!! Discover the scientist in you. Are you excited to create something immediately without getting into too much subject theory which bores you? Then you have landed at the right course. Research has shown that theoretical learning leads to decrease in interest in the subject and is one of the biggest hindrances to learn new things or new Technology. That's why we have created a course for every body where you start building applications and learn theory along with it.


Report: 59% of employed data scientists learned skills on their own or via a MOOC

@machinelearnbot

The majority of employed data scientists gained their skills through self-learning or a Massive Open Online Course (MOOC) rather than a traditional computer science degree, according to a survey from data scientist community Kaggle, which was acquired by Google Cloud earlier this year. Some 32% of full-time data scientists started learning machine learning or data science through a MOOC, while 27% said that they began picking up the needed skills on their own, the 2017 State of Data Science & Machine Learning Survey report found. Some 30% got their start in data science at a university, according to the survey of more than 16,000 people in the field. More than half of currently employed data scientists still use MOOCs for ongoing education and skillbuilding, the report found, demonstrating the potential of these courses for helping people gain real world skills. Data scientist took the no. 1 spot in Glassdoor's Best Jobs in America list in 2016 and 2017, and reports a median base salary of $110,000.



Deep learning is a new chapter for every sector: Andrew Ng, Coursera

@machinelearnbot

The co-founder of online education platform Coursera has made it his mission to build talent for AI through his new project, deeplearning.ai. Andrew is preparing courses on deep-learning--advanced AI inspired by the human brain's neural networks--that will be available on Coursera. In an interview with ET's J Vignesh, the former chief scientist at Baidu also spoke about how technology disruption can help countries like India leapfrog and take a lead in the new world. Edited excerpts: How are we progressing towards the concept of singularity, or general intelligence, from sector-specific artificial intelligence? That is hard to project.


Online Learning of Power Transmission Dynamics

arXiv.org Machine Learning

We consider the problem of reconstructing the dynamic state matrix of transmission power grids from time-stamped PMU measurements in the regime of ambient fluctuations. Using a maximum likelihood based approach, we construct a family of convex estimators that adapt to the structure of the problem depending on the available prior information. The proposed method is fully data-driven and does not assume any knowledge of system parameters. It can be implemented in near real-time and requires a small amount of data. Our learning algorithms can be used for model validation and calibration, and can also be applied to related problems of system stability, detection of forced oscillations, generation re-dispatch, as well as to the estimation of the system state.