prediction


cortexlabs/cortex

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Cortex is an open source platform that takes machine learning models--trained with nearly any framework--and turns them into production web APIs in one command. Autoscaling: Cortex automatically scales APIs to handle production workloads. Multi framework: Cortex supports TensorFlow, PyTorch, scikit-learn, XGBoost, and more. CPU / GPU support: Cortex can run inference on CPU or GPU infrastructure. Rolling updates: Cortex updates deployed APIs without any downtime.


SAS Tutorial Interpreting Machine Learning Models in SAS

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SUBSCRIBE TO THE SAS USERS YOUTUBE CHANNEL #SASUsers #LearnSAS https://www.youtube.com/SASUsers?sub_... ABOUT SAS SAS is a trusted analytics powerhouse for organizations seeking immediate value from their data. A deep bench of analytics solutions and broad industry knowledge keep our customers coming back and feeling confident. With SAS, you can discover insights from your data and make sense of it all. Identify what's working and fix what isn't.


Let's get phygital: Most disruptive tech of 2020

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Tech service provider NTT released a report on Monday outlining the top digital disruption predictions for 2020. In the report, NTT CTO Ettienne Reinecke highlighted five specific disruptive technologies expected to impact 2020. After gathering global insights on intelligent tech solutions from clients, NTT experts determined the future's most impactful disruptive technologies. Gartner's IT glossary defines digital disruption as "an effect that changes the fundamental expectations and behaviors in a culture, market, industry or process that is caused by, or expressed through, digital capabilities, channels or assets." SEE: Digital transformation: An IT pro's guide (free PDF) (TechRepublic) While the word disruption may have a negative connotation, digital disruption is a positive movement for the tech world.


How Can AI Future-Proof Small Business Finances?

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The future can be hazardous to your financial health. Your numbers might look good today, but what if tomorrow brought a cluster of payments due that suddenly dropped your cash flow into the red? What if an unexpected event or change in circumstances impacted your finances, requiring you to sacrifice your values or lifestyle? What if such extenuating circumstances eroded your bank account balance with exorbitant overdraft fees? At a time when 40 percent of Americans wouldn't be able to cover a $400 emergency, the unfortunate reality is that millions of people face financial challenges like this every day.


Artificial Intelligence Uses ECGs to Predict A-Fib Risk

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TUESDAY, Nov. 12, 2019 (HealthDay News) -- In two studies, artificial intelligence was used with electrocardiogram (ECG) results to identify patients who are at increased risk for a potentially dangerous irregular heartbeat, and those more likely to die within a year, researchers say. Using more than 2 million ECG results gathered over three decades, the team created "deep neural networks" that predict future events from an ECG. In one study, researchers used 1.1 million ECGs that did not find atrial fibrillation (a-fib) in more than 237,000 patients to assess the network's ability to predict the heart rhythm disorder before it develops. A-fib increases the risk of heart attack and stroke. Among the top 1% of high-risk patients as predicted by the neural network, one-third were diagnosed with a-fib within a year.


Instantgo: Fate, Karma, Soul, Spirituality, Prayer - Apps on Google Play

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Discover and share your fate, soul, karma and connect with the people you love in a unique way. Instantgo provides you with unique access to a community of like-minded people where senses and spirituality play a major role. Leverage unique features to share and broaden your karma, soul, and knowledge. Connect and share your emotions with your loved ones and people from all over the world. Get answers to some of your most important questions and explore our community of experts that can guide you through every step.


InterVision 2020 Predictions: Solving for Data in States of Storage, Deployment and Usage : @VMblog

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As I look back at 2019 and the changes that shaped the IT landscape this year, we saw more widespread adoption of solutions that both run on and regurgitate datasets in efforts to improve business intelligence. We are increasingly seeing the role of data in enabling futuristic technologies like data analytics, machine learning, Artificial Intelligence (AI) and the Internet of Things (IoT). With 2020 on the horizon, everything is trending toward how and why companies use their data to serve consumers, make decisions for competitiveness, and iterate operations to improve profitability. For this reason, I think that one of the biggest challenges of 2020 will be how companies can secure a reliable storage, deployment and usage posture for their datasets. Datasets need to move around fast during usage and deployment stages, but also securely.


20 Predictions for 2020 from AI to Data Management Registration

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AI, machine learning, cloud, self-service, data governance, etc…there is no shortage of buzzwords in data today. Every organization is seeking to outpace their competition by leveraging data to drive differentiation for their business. To win this race, companies are building up data science teams, investing in faster/more scalable cloud data platforms and utilizing the growing variety of publicly available datasets and algorithms. How do you stay ahead of what's next and help drive the successful adoption of new technology and processes within your organization?


Clinical Risk Score for Predicting Recurrence Following a Cerebral Ischemic Event

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Introduction: Recurrent stroke has a higher rate of death and disability. A number of risk scores have been developed to predict short-term and long-term risk of stroke following an initial episode of stroke or transient ischemic attack (TIA) with limited clinical utilities. In this paper, we review different risk score models and discuss their validity and clinical utilities. Methods: The PubMed bibliographic database was searched for original research articles on the various risk scores for risk of stroke following an initial episode of stroke or TIA. The validation of the models was evaluated by examining the internal and external validation process as well as statistical methodology, the study power, as well as the accuracy and metrics such as sensitivity and specificity.


Machine learning is changing the way retailers do business

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In 2002, Target hired statistician Andrew Pole. His job was to use predictive analytics -- a form of statistics that makes predictions by observing data trends -- to help the retail giant market certain products to certain groups of people. Along those lines, Pole's first task was to identify pregnant women -- specifically women in their second trimester. As Target's marketing team explained to him, new parents are extremely valuable customers whose brand loyalty tends to change when they have kids because they purchase things they probably weren't purchasing before -- like diapers, formula, baby clothes, etc. New parents also tend to be physically exhausted and therefore more prone to do all of their shopping at one place.