This paper introduces Zap, a generic machine learning pipeline for making predictions based on online user behavior. Zap combines well known techniques for processing sequential data with more obscure techniques such as Bloom filters, bucketing, and model calibration into an end-to-end solution. The pipeline creates website- and task-specific models without knowing anything about the structure of the website. It is designed to minimize the amount of website-specific code, which is realized by factoring all website-specific logic into example generators. New example generators can typically be written up in a few lines of code.

I have come across a couple of resources about dimensionality reduction techniques. This topic is definitively one of the most interesting ones, and it is great to think that there are algorithms able to reduce the number of features by choosing the most important ones that still represent the entire dataset. One of the advantages pointed out by authors is that these algorithms can improve the results of a classification task. In this post, I am going to verify this statement using a Principal Component Analysis ( PCA) to try to improve the classification performance of a neural network over a dataset. Does PCA really improve classification outcome?

Background: Early and appropriate empiric antibiotic treatment of patients suspected of having sepsis is associated with reduced mortality. This problem is particularly severe for children in developing country settings. We hypothesized that by applying machine learning approaches to readily collected patient data, it would be possible to obtain actionable and patient-specific predictions for antibiotic-susceptibility. If sufficient discriminatory power can be achieved, such predictions could lead to substantial improvements in the chances of choosing an appropriate antibiotic for empiric therapy, while minimizing the risk of increased selection for resistance due to use of antibiotics usually held in reserve. Methods and Findings: We analyzed blood culture data collected from a 100-bed children's hospital in North-West Cambodia between February 2013 and January 2016. Clinical, demographic and living condition information for each child was captured with 35 independent variables.

In the past few years, the area of Machine Learning (ML) has witnessed tremendous advancements, becoming a pervasive technology in a wide range of applications. One area that can significantly benefit from the use of ML is Combinatorial Optimization. The three pillars of constraint satisfaction and optimization problem solving, i.e., modeling, search, and optimization, can exploit ML techniques to boost their accuracy, efficiency and effectiveness. In this survey we focus on the modeling component, whose effectiveness is crucial for solving the problem. The modeling activity has been traditionally shaped by optimization and domain experts, interacting to provide realistic results. Machine Learning techniques can tremendously ease the process, and exploit the available data to either create models or refine expert-designed ones. In this survey we cover approaches that have been recently proposed to enhance the modeling process by learning either single constraints, objective functions, or the whole model. We highlight common themes to multiple approaches and draw connections with related fields of research.

Artificial Intelligence (AI) has gained considerable prominence over the last decade fueled by a number of high profile applications in Autonomous Vehicles (AV), intelligent robots, image and speech recognition, automatic translations, medical and law usage as well as beating champions in games like chess, Jeopardy, GO and poker [1]. The successes of AI are based on the utilization of algorithms capable of learning by trial and error and improving their performance over time, not just by step-by-step coding instructions based on logic, if-then rules and decision trees, which is the sphere of traditional programming. In light of the above, AI found applications in the field of forecasting and a considerable amount of research has been conducted on how a special class of it, utilizing Machine Learning methods (ML) and especially Neural Networks (NNs), can be exploited to improve time series predictions. Literally hundreds of papers propose new ML algorithms, suggesting methodological advances and accuracy improvements [2–8]. Yet, limited objective evidence is available regarding their relative performance as a standard forecasting tool [9–12].

By and large the behavior of stochastic gradient is regarded as a challenging problem, and it is often presented in the framework of statistical machine learning. This paper offers a novel view on the analysis of on-line models of learning that arises when dealing with a generalized version of stochastic gradient that is based on dissipative dynamics. In order to face the complex evolution of these models, a systematic treatment is proposed which is based on energy balance equations that are derived by means of the Caldirola-Kanai (CK) Hamiltonian. According to these equations, learning can be regarded as an ordering process which corresponds with the decrement of the loss function. Finally, the main results established in this paper is that in the case of quasi-periodic environments, where the pattern novelty is progressively limited as time goes by, the system dynamics yields an asymptotically consistent solution in the weight space, that is the solution maps similar patterns to the same decision.

This article shows you how we created an xkcd.com We can predict the topic of the comic from the description of the comic. My circle of friends has a huge nerd crush on Randall Munroe, the author of the xkcd comics, and books like what if?. Mary Kate MacPherson took the initiative, and scraped the transcripts for every comic, and used my patent analyzer code to turn the transcripts into embedding vectors. She then tossed the vectors and labels into tensorflow and crunched the data down into clusters using t-SNE. Those of you who know xkcd.com will be well aware that the comics are numbered sequentially.

Editor's Note: This article originally appeared on tensorflow.org and is being republished under the guidelines of the Creative Commons Attribution 3.0 License (more legal details at the end of the article). This tutorial is intended for readers who are new to both machine learning and TensorFlow. If you already know what MNIST is, and what softmax (multinomial logistic) regression is, you might prefer this faster paced tutorial. Be sure to install TensorFlow before starting either tutorial. When one learns how to program, there's a tradition that the first thing you do is print "Hello World." Just like programming has Hello World, machine learning has MNIST.

Today's blog post is inspired by a question from PyImageSearch reader, Leonard Bogdonoff. Hey Adrian, can you go into identity clustering? I have a dataset of photos and I can't seem to pinpoint how I would process them to identify the unique people. Such an application of "face clustering" or "identity clustering" could be used to aid to law enforcement. Consider a scenario where two perpetrators rob a bank in a busy city such as Boston or New York.

Machine learning algorithms can help scientists predict chemical toxicity to a similar degree of accuracy as animal testing, according to a paper published this week in Toxicological Sciences. A whopping €3bn (over $3.5bn) is spent every year to study how the negative impacts of chemicals on animals like rats, rabbits or monkeys. The top nine most frequently tested safety experiments resulted in the death of the poor critters 57 per cent of the time in Europe in 2011. By using software, chemists may be able to spend less on animal testing and save more creatures. To demonstrate this, first, a team of researchers scoured through a range of databases to label 80,908 different chemicals.