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My PC vs Azure ML Compute -- GO!

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A few weeks ago, I created a YouTube video on connecting Microsoft Visual Studio Code to a Jupyter Notebook running on a Compute resource within Azure (via Azure ML Studio). When I was making the video, I kept wondering how much faster that remote server really is. So I decided to test it. I'm not going to make you wade through my exploratory data analysis, or even loading my data. That's not germane to my purpose, which is just to show you the difference between running a significant piece of code on my personal computer and comparing it to Compute on Azure.


Calculate Maximum Likelihood Estimator with Newton-Raphson Method using R

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In statistical modeling, we have to calculate the estimator to determine the equation of your model. The problem is, the estimator itself is difficult to calculate, especially when it involves some distributions like Beta, Gamma, or even Gompertz distribution. Maximum Likelihood Estimator (MLE) is one of many methods to calculate the estimator for those distributions. In this article, I will give you some examples to calculate MLE with the Newton-Raphson method using R. Newton-Raphson method is an iterative procedure to calculate the roots of function f. The goal of this method is to make the approximated result as close as possible with the exact result (that is, the roots of the function).


Predicting Credit Card Defaults with Machine Learning

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Sometimes the best model is the simplest. The model with minimal manipulation yielded the highest recall score of 0.95. After feature selection and hyperparameter tuning, recall decreased to 0.79. Overfitting means the model is strong at predicting the data on which it was trained, but weak at generalizing to unseen data. The validation score is similar to the test score, so we know it's performing similarly on completely unseen.


Sensyne Health receives UK approval for COVID-19 risk prediction tool

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Clinical AI company Sensyne Health has received regulatory approval in the UK for its SYNE-COV machine learning algorithm for COVID-19 risk prediction. SYNE-COV analyses over 60 variables in the patient electronic health record to generate a prediction of the likelihood of a COVID-19 positive patient developing severe disease, requiring ventilation or admission to intensive care. It provides the risk prediction, together with an explanation of the result, to help clinicians manage patients admitted to hospital with COVID-19 infection. The SYNE-COV product was developed in collaboration between Sensyne and the Chelsea & Westminster Hospitals NHS Foundation Trust. This is the first algorithm developed from the SENSE clinical and operational algorithm engine to achieve UK regulatory approval.


Why 0.9? Towards Better Momentum Strategies in Deep Learning.

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Momentum is a widely-used strategy for accelerating the convergence of gradient-based optimization techniques. Momentum was designed to speed up learning in directions of low curvature, without becoming unstable in directions of high curvature. In deep learning, most practitioners set the value of momentum to 0.9 without attempting to further tune this hyperparameter (i.e., this is the default value for momentum in many popular deep learning packages). However, there is no indication that this choice for the value of momentum is universally well-behaved. Within this post, we overview recent research indicating that decaying the value of momentum throughout training can aid the optimization process.


Wearables and AI Transforming Healthcare Diagnosis

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Artificial intelligence has a significant role to play in the increasing digitization and automation of various industries. Advanced technologies like AI, machine learning, NLP, etc., have increased the pace and quality of digital transformation. Life becomes convenient and easier with these technological advancements. Healthcare is an important industry that has been crippling with shortcomings and quality erosion. Although, many healthcare providers have invested in technology to improve the quality and pace of treatment.


We should be less worried about robots killing jobs than being forced to work like robots

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More than outright destroying jobs, automation is changing employment in ways that will weigh on workers. The big picture: Right now, we should be less worried about robots taking human jobs than people in low-skilled positions being forced to work like robots. What's happening: In a report released late last week about the post-COVID-19 labor force, McKinsey predicted 45 million U.S. workers would be displaced by automation by the end of the decade, up from 37 million projected before the pandemic. Yes, but: McKinsey notes that despite the displacements, the total number of jobs is projected to increase. The catch: McKinsey finds that while the total number of jobs will increase, "nearly all net job growth over the next decade is projected to be in high-wage occupations" -- which is not good news for workers with low job skills.


Global governance of AI and a UN Parliamentary Assembly

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Artificial Intelligence (AI) is playing an increasingly dominant role in the lives of people around the world. As yet, however, it is subject to remarkably little governance, let alone global governance. There is a strong case for the establishment of a UN regime on Artificial Intelligence. Such a regime should include a supervisory body that can provide a democratic input. Artificial Intelligence has immense value to offer to humanity, such as improved efficiency, new capabilities, and solutions for complex problems.


Data science, machine learning, and artificial intelligence

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With the ever-increasing volume, variety, and velocity of available data, scientific disciplines have provided us with advanced mathematical tools, processes, and algorithms enabling us to use this data in meaningful ways. Data science (DS), machine learning (ML), and artificial intelligence (AI) are three such disciplines. A question that frequently comes up in many data-related discussions is what the difference between DS, ML, and AI is? Can they be compared? Depending on who you talk to, how many years of experience they have had, and what projects they have worked on, you may get widely different answers to the above question. In this blog, I will attempt to answer this based on my research, academic, and industry experience; and having facilitated numerous conversations on the topic.


Digital transformation: 5 areas where Artificial Intelligence (AI) fits now

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IDC estimates that global budgets for Artificial Intelligence will double over the next four years, to $110 billion in 2024, per its recent Worldwide Artificial Intelligence Spending Guide. "Companies will adopt AI -- not just because they can, but because they must," IDC's AI program vice president Ritu Jyoti noted. "AI is the technology that will help businesses to be agile, innovate, and scale." The arrival of AI capabilities in the enterprise is no longer theoretical. "The last year has demonstrated a rapid acceleration that has changed the question from'Where do artificial intelligence technologies fit within our organization?'