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On Moving from Statistics to Machine Learning, the Final Stage of Grief

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I've spent the last few months preparing for and applying for data science jobs. It's possible the data science world may reject me and my lack of both experience and a credential above a bachelors degree, in which case I'll do something else. Regardless of what lies in store for my future, I think I've gotten a good grasp of the mindset underlying machine learning and how it differs from traditional statistics, so I thought I'd write about it for those who have a similar background to me considering a similar move.1 This post is geared toward people who are excellent at statistics but don't really "get" machine learning and want to understand the gist of it in about 15 minutes of reading. If you have a traditional academic stats backgrounds (be it econometrics, biostatistics, psychometrics, etc.), there are two good reasons to learn more about data science: The world of data science is, in many ways, hiding in plain sight from the more academically-minded quantitative disciplines.


Deep Learning: Big Data Intelligence

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Big Data Intelligence: A machine learning approach inspired by the human brain, Deep Learning is taking many industries by storm. Empowered by the latest generation of commodity computing, Deep Learning begins to derive significant value from Big Data. It has already radically improved the computer's ability to recognize speech and identify objects in images, two fundamental hallmarks of human intelligence.


Kaggle: Where data scientists learn and compete

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Data science is typically more of an art than a science, despite the name. You start with dirty data and an old statistical predictive model and try to do better with machine learning. Nobody checks your work or tries to improve it: If your new model fits better than the old one, you adopt it and move on to the next problem. When the data starts drifting and the model stops working, you update the model from the new dataset. Doing data science in Kaggle is quite different.


An Interactive Visualization of Autoencoders, Built with Tensorflow.js

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Introducing Anomagram - An interactive tool that lets you train and evaluate an autoencoder for the task of anomaly detection on ECG data. Across many business use cases that generate data, it is frequently desirable to automatically identify data samples that deviate from "normal". In many cases, these deviations are indicative of issues that need to be addressed. For example, an abnormally high cash withdrawal from a previously unseen location may be indicative of fraud. An abnormally high CPU temperature may be indicative of impending hardware failure.


Global Big Data Conference

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As real-world AI deployments increase, IBM says the contributions can help ensure they're fair, secure and trustworthy. IBM on Monday announced it's donating a series of open-source toolkits designed to help build trusted AI to a Linux Foundation project, the LF AI Foundation. As real-world AI deployments increase, IBM says the contributions can help ensure they're fair, secure and trustworthy. "Donation of these projects to LFAI will further the mission of creating responsible AI-powered technologies and enable the larger community to come forward and co-create these tools under the governance of Linux Foundation," IBM said in a blog post, penned by Todd Moore, Sriram Raghavan and Aleksandra Mojsilovic. Specifically, IBM is contributing the AI Fairness 360 Toolkit, the Adversarial Robustness 360 Toolbox and the AI Explainability 360 Toolkit.


IoT Anomaly detection - algorithms, techniques and open source implementation

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Learning classifiers for misuse and anomaly detection using a bag of system calls representation. Anomaly detection in health data based on deep learning. Abnormal human activity recognition using SVM based approach. Anomaly detection of gas turbines based on normal pattern extraction. Contextual anomaly detection for a critical industrial system based on logs and metrics.


Top 5 latest trends in Artificial Intelligence in 2020

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AI is not something new. Over the years, it has made immense advancement in every field like healthcare, manufacturing, law, finance, retail, real estate, accountancy, digital marketing, and several other areas. Each one is computational and irrefutable from upcoming changes in the system. AI algorithms have proved dangerous in terms of Skynet images, the matrix, robot Apocalypse, and technological unemployment. A wide range of diverse AI patterns like autonomous systems, chatbots, document classification, advanced predictive analytics solutions have made human labor jobless.


Abolish the #TechToPrisonPipeline

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The authors of the Harrisburg University study make explicit their desire to provide "a significant advantage for law enforcement agencies and other intelligence agencies to prevent crime" as a co-author and former NYPD police officer outlined in the original press release.[38] At a time when the legitimacy of the carceral state, and policing in particular, is being challenged on fundamental grounds in the United States, there is high demand in law enforcement for research of this nature, research which erases historical violence and manufactures fear through the so-called prediction of criminality. Publishers and funding agencies serve a crucial role in feeding this ravenous maw by providing platforms and incentives for such research. The circulation of this work by a major publisher like Springer would represent a significant step towards the legitimation and application of repeatedly debunked, socially harmful research in the real world. To reiterate our demands, the review committee must publicly rescind the offer for publication of this specific study, along with an explanation of the criteria used to evaluate it. Springer must issue a statement condemning the use of criminal justice statistics to predict criminality and acknowledging their role in incentivizing such harmful scholarship in the past. Finally, all publishers must refrain from publishing similar studies in the future.


How Machine Learning can boost your Predictive Analytics

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Every business seeks to grow. But only a handful of companies that successfully actualize this vision do so through data-based decision making. And to make these informed decisions, companies have been using machine learning-based predictive analytics. Predictive analytics is predicting future outcomes based on historical and current data. It uses various statistical and data modeling techniques to analyze past data, identify trends, and help make informed business decisions.


How Machine Learning can boost your Predictive Analytics

#artificialintelligence

Every business seeks to grow. But only a handful of companies that successfully actualize this vision do so through data-based decision making. And to make these informed decisions, companies have been using machine learning-based predictive analytics. Predictive analytics is predicting future outcomes based on historical and current data. It uses various statistical and data modeling techniques to analyze past data, identify trends, and help make informed business decisions.