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A Complete Anomaly Detection Algorithm From Scratch in Python: Step by Step Guide

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Anomaly detection can be treated as a statistical task as an outlier analysis. But if we develop a machine learning model, it can be automated and as usual, can save a lot of time. There are so many use cases of anomaly detection. Credit card fraud detection, detection of faulty machines, or hardware systems detection based on their anomalous features, disease detection based on medical records are some good examples. There are many more use cases.


Machine learning model generates realistic seismic waveforms

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LOS ALAMOS, N.M., April 22, 2021--A new machine-learning model that generates realistic seismic waveforms will reduce manual labor and improve earthquake detection, according to a study published recently in JGR Solid Earth. "To verify the efficacy of our generative model, we applied it to seismic field data collected in Oklahoma," said Youzuo Lin, a computational scientist in Los Alamos National Laboratory's Geophysics group and principal investigator of the project. "Through a sequence of qualitative and quantitative tests and benchmarks, we saw that our model can generate high-quality synthetic waveforms and improve machine learning-based earthquake detection algorithms." Quickly and accurately detecting earthquakes can be a challenging task. Visual detection done by people has long been considered the gold standard, but requires intensive manual labor that scales poorly to large data sets.


what is machine learning ?

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What are the basics of machine learning? Everything you need to know -- this section clarifies machine learning and its similarities to artificial intelligence, why it functions and the significance of it. Machine learning is a branch of artificial intelligence that seeks to learn from data and make predictions using these methods. Machine learning is most often used as a computer algorithm, but can also be viewed as machine software, rather than software that runs on computers. The automated conclusion or prediction drawn from the analysis of data by machine learning algorithms is called the "analytical result".


How AI Localization Differs from Traditional Localization

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Localizing content delivers strong business benefits. According to white paper released by Pactera EDGE and Nimdzi Insights, companies that localize the user experience see a 100%–400% increase in sales, and by localizing into just 10 languages, a brand's message will effectively reach 90% of online customers. As brands appreciate the business benefits of localization, they are increasingly turning to artificial intelligence to make localization more effective. This is true especially for large, complex, multinational businesses that need to adapt multiple products and services across hundreds of geographic markets and cultures. In fact, we believe AI can unlock hyperlocal and hyper-personalized experiences that are culturally aware, as my colleague Ilia Shifrin blogged recently.


Teaching a Neural Network to Play Cards

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I chose this game because the difficulty level is just about right so that it is easy to learn but it still has some twists that make it interesting. The result of the project is an Android app where you can play against AI-based opponents with adjustable difficulty level. Queen of Spades is a trick-taking card game that is related to Hearts. The game is for 4 players and is played with a deck of 32 cards. There are four suits: Hearts, Diamonds, Spades, and Clubs. Each suit has the following ranks in descending order: Ace, King, Queen, Jack, 10, 9, 8, and 7.


What is data poisoning? Attacks thatcorrupt machine learning models

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Machine learning adoption exploded over the past decade, driven in part by the rise of cloud computing, which has made high performance computing and storage more accessible to all businesses. As vendors integrate machine learning into products across industries, and users rely on the output of its algorithms in their decision making, security experts warn of adversarial attacks designed to abuse the technology. Most social networking platforms, online video platforms, large shopping sites, search engines and other services have some sort of recommendation system based on machine learning. The movies and shows that people like on Netflix, the content that people like or share on Facebook, the hashtags and likes on Twitter, the products consumers buy or view on Amazon, the queries users type in Google Search are all fed back into these sites' machine learning models to make better and more accurate recommendations. It's not news that attackers try to influence and skew these recommendation systems by using fake accounts to upvote, downvote, share or promote certain products or content.


Machine learning model generates realistic seismic waveforms

#artificialintelligence

LOS ALAMOS, N.M., April 22, 2021--A new machine-learning model that generates realistic seismic waveforms will reduce manual labor and improve earthquake detection, according to a study published recently in JGR Solid Earth. "To verify the efficacy of our generative model, we applied it to seismic field data collected in Oklahoma," said Youzuo Lin, a computational scientist in Los Alamos National Laboratory's Geophysics group and principal investigator of the project. "Through a sequence of qualitative and quantitative tests and benchmarks, we saw that our model can generate high-quality synthetic waveforms and improve machine learning-based earthquake detection algorithms." Quickly and accurately detecting earthquakes can be a challenging task. Visual detection done by people has long been considered the gold standard, but requires intensive manual labor that scales poorly to large data sets.


Machine learning model generates realistic seismic waveforms

#artificialintelligence

LOS ALAMOS, N.M., April 22, 2021--A new machine-learning model that generates realistic seismic waveforms will reduce manual labor and improve earthquake detection, according to a study published recently in JGR Solid Earth. "To verify the e?cacy of our generative model, we applied it to seismic?eld data collected in Oklahoma," said Youzuo Lin, a computational scientist in Los Alamos National Laboratory's Geophysics group and principal investigator of the project. "Through a sequence of qualitative and quantitative tests and benchmarks, we saw that our model can generate high-quality synthetic waveforms and improve machine learning-based earthquake detection algorithms." Quickly and accurately detecting earthquakes can be a challenging task. Visual detection done by people has long been considered the gold standard, but requires intensive manual labor that scales poorly to large data sets.


What is Machine Learning?

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Machine learning is a branch of artificial intelligence (AI) focused on building applications that learn from data and improve their accuracy over time without being programmed to do so. In data science, an algorithm is a sequence of statistical processing steps. In machine learning, algorithms are'trained' to find patterns and features in massive amounts of data in order to make decisions and predictions based on new data. The better the algorithm, the more accurate the decisions and predictions will become as it processes more data. Today, examples of machine learning are all around us. Digital assistants search the web and play music in response to our voice commands.


AI predicts effective drug combinations to fight complex diseases faster

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Finding new ways to repurpose or combine existing drugs has proved to be a powerful tool to treat complex diseases. Drugs used to treat one type of cancer, for instance, have effectively strengthened treatments for other cancer cells. Complex malignant tumors often require a combination of drugs, or "drug cocktails," to formulate a concerted attack on multiple cell types. Drug cocktails can not only help stave off drug resistance but also minimize harmful side effects. But finding an effective combination of existing drugs at the right dose is extremely challenging, partly because there are near-infinite possibilities.