Machine Learning


Ensemble Methods in One Picture

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Generate the Base Learners: Choose any combination of base learners, based on accuracy and diversity. Each base learner can produce more than one predictive model, if you change variables such as case weights, guidance parameters, or input space partitions. The result is a computational "average" of sorts (which is much more complex than the regular arithmetic average). Generate the Base Learners: Choose any combination of base learners, based on accuracy and diversity. Each base learner can produce more than one predictive model, if you change variables such as case weights, guidance parameters, or input space partitions.


CS231n Convolutional Neural Networks for Visual Recognition

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In the previous sections we've discussed the static parts of a Neural Networks: how we can set up the network connectivity, the data, and the loss function. This section is devoted to the dynamics, or in other words, the process of learning the parameters and finding good hyperparameters. In theory, performing a gradient check is as simple as comparing the analytic gradient to the numerical gradient. In practice, the process is much more involved and error prone. This requires you to evaluate the loss function twice to check every single dimension of the gradient (so it is about 2 times as expensive), but the gradient approximation turns out to be much more precise. To see this, you can use Taylor expansion of \(f(x h)\) and \(f(x-h)\) and verify that the first formula has an error on order of \(O(h)\), while the second formula only has error terms on order of \(O(h 2)\) (i.e. it is a second order approximation). What are the details of comparing the numerical gradient \(f'_n\) and analytic gradient \(f'_a\)? That is, how do we know if the two are not compatible? You might be temped to keep track of the difference \(\mid f'_a - f'_n \mid \) or its square and define the gradient check as failed if that difference is above a threshold.


28 Statistical Concepts Explained in Simple English - Part 18

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This resource is part of a series on specific topics related to data science: regression, clustering, neural networks, deep learning, decision trees, ensembles, correlation, Python, R, Tensorflow, SVM, data reduction, feature selection, experimental design, cross-validation, model fitting, and many more. To keep receiving these articles, sign up on DSC. Below is the last article in the series Statistical Concepts Explained in Simple English. The full series is accessible here. To make sure you keep getting these emails, please add [email protected] to your address book or whitelist us.


Deep learning AI may identify atrial fibrillation from a normal rhythm ECG - Times of India

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Although early and requiring further research before implementation, the findings could aid doctors investigating unexplained strokes or heart failure, enabling appropriate treatment. Researchers have trained an artificial intelligence model to detect the signature of atrial fibrillation in 10-second electrocardiograms (ECG) taken from patients in normal rhythm. The study, involving almost 181,000 patients and published in The Lancet, is the first to use deep learning to identify patients with potentially undetected atrial fibrillation and had an overall accuracy of 83%. Atrial fibrillation is estimated to affect 2.7–6.1 million people in the United States and is associated with increased risk of stroke, heart failure and mortality. It is difficult to detect on a single ECG because patients' hearts can go in and out of this abnormal rhythm, so atrial fibrillation often goes undiagnosed.


Google open-sources Live Transcribe's speech engine

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The company hopes doing so will let any developer deliver captions for long-form conversations. The source code is available now on GitHub. Google released Live Transcribe in February. The tool uses machine learning algorithms to turn audio into real-time captions. Unlike Android's upcoming Live Caption feature, Live Transcribe is a full-screen experience, uses your smartphone's microphone (or an external microphone), and relies on the Google Cloud Speech API.


America Can Stop China from Dominating Artificial Intelligence--And Should

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China, writes Amy Webb in Inc., has been "building a global artificial intelligence empire, and seeding the tech ecosystem of the future." It has been particularly successful, Webb, the founder of the Future Today Institute, believes. "China is poised to become its undisputed global leader, and that will affect every business," she notes. Not everyone shares Webb's assessment that Chinese researchers are in the lead. America, after all, is home to most leading AI tech.


How data can predict which employees are about to quit: Rather than relying on exit interviews and their comparisons to occasional employee surveys to determine engagement, organizations can turn instead to big data and advanced analytics to identify those workers at greatest risk of quitting.

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Rather than relying on exit interviews and their comparisons to occasional employee surveys to determine engagement, organizations can turn instead to big data and advanced analytics to identify those workers at greatest risk of quitting. A new Harvard Business Review article outlines how applying machine learning algorithms to turnover data and employee information can provide a much more accurate picture of workplace satisfaction. This measure of "turnover propensity" comprised two main indicators: turnover shocks, which are organizational and personal events that cause workers to reconsider their jobs, and job embeddedness, which describes an employee's social ties in their workplace and interest in the work they do. Though achieving this kind of "proactive anticipation" will require a sizable investment of time and effort to develop the necessary data and algorithms, the payoff will likely be worth it: "Leaders can proactively engage valued employees at risk of leaving through interviews, to better understand how the firm can increase the odds that they stay," per HBR. More articles on leadership and management: Can your anesthesia department handle NORA?


Variable selection using LASSO

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This is a Lasso; it is used to pick and capture animals. As a non-native English speaker, my first exposure to this word is in supervised learning. In this LASSO data science tutorial, we discuss the strengths of the Lasso logistic regression by stepping through how to apply this useful statistical method for classification problems in R and how the Lasso can be "similarly" used to pick and select input variables that are relevant to the classification problem at hand. Data analysts and data scientists use different regression methods for different kinds of analytics problems. One of the most talked-about methods is the Lasso.


Data Science, the Good, the Bad, and the… Future

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How often do you think you're touched by data science in some form or another? Finding your way to this article likely involved a whole bunch of data science (whooaa). To simplify things a bit, I'll explain what data science means to me. "Data Science is the art of applying scientific methods of analysis to any kind of data so that we can unlock important information." If we unpack that, all data science really means is to answer questions by using math and science to go through data that's too much for our brains to process.


Data Science, the Good, the Bad, and the… Future - Knowlab

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How often do you think you're touched by data science in some form or another? Finding your way to this article likely involved a whole bunch of data science (whooaa). To simplify things a bit, I'll explain what data science means to me. "Data Science is the art of applying scientific methods of analysis to any kind of data so that we can unlock important information." If we unpack that, all data science really means is to answer questions by using math and science to go through data that's too much for our brains to process.