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Significance of Synthetic Data in Machine Learning Engineering

significance of synthetic data in machine learning engineering

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Not everything that is inorganic, manufactured or synthetic is fake or inferior. This assertion is particularly true when it comes to synthetic data in the context of machine learning. Simulated data is not only useful but also more practical when compared to real or actual data, in some cases.In the field of machine learning, synthetic data is crucial to ensure that an AI system has been trained sufficiently before it is deployed. Machine learning engineering, the process of producing a machine learning (ML) model with the help of software engineering and data science principles, will encounter critical difficulties without synthetic data.SEE ALSO: HOW MACHINE LEARNING AND AI WILL IMPACT ENGINEERINGWhat is synthetic data?Synthetic data, according to Gartner, is “data generated by applying a sampling technique to real-world data or by creating simulation scenarios where models and processes interact to create completely new data not directly taken from the real world.” In short, it is information borne out of simulation and not by direct measurement. It is different from data collected through an actual survey, visual capture, and other direct data gathering methods.It is important to emphasize, however, that synthetic data is not false information. While it …

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Bristol Myers, insitro ally to apply machine learning to ALS R&D

bristol myers, insitro ally to apply machine learning to als r&d

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Bristol Myers Squibb has teamed up with insitro to develop treatments for neurodegenerative disorders. The deal is worth $50 million upfront but could balloon in value to more than $2 billion if all the milestones are hit.  Under the terms of the deal, insitro will use its platform to create induced pluripotent stem cell-derived disease models for amyotrophic lateral sclerosis (ALS) and frontotemporal dementia (FTD). The platform uses machine learning, human genetics and functional genomics to generate in vitro models that shed light on disease progression and segment the patient population.

After generating such insights, insitro will use its machine learning-enabled approach to drug discovery to advance candidates against ALS and FTD. The agreement gives Bristol Myers the option to select a number of targets identified by insitro to take into the clinic and beyond.

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Bristol Myers is paying $50 million for that option. The deal also commits Bristol Myers to more than $2 billion in discovery, development, regulatory and commercial milestones, plus royalties. In return, insitro will apply …

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Comparing Different Programming Languages For Machine Learning

comparing different programming languages for machine learning

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How has machine learning changed the world? Let us have a look at it! You can find it in the recommendation system of amazon, youtube etc. It is used in defence in UAVs (unguided air vehicles) with object detection. It is also used to understand text sentiments on social media platforms in important events like elections etc. All of these things are coded, obviously. And trust me if you are a beginner, with the time you can build such things too. 

And now you may ask which language would really be the apt one to start this journey — because there are numerous languages available for the same. So let us delve deeper into the topic to get a better understanding of the topic.

There are a lot of programming languages which support machine learning libraries, and one may think which one to choose to get the best outcomes for the same. Trying to choose the right language for your own self without any prior information is like being a kid in the toy shop who is confused and fascinated at the same time. So I would advise you to read this article which will give some clarity on the topic. …

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Machine learning helps hunt for COVID-19 therapies

machine learning helps hunt for covid-19 therapies

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The colored regions mark the regions where potential drugs bind to the coronavirus’s main protease, shown in white, as predicted by MSU deep learning models. Credit: Guowei Wei’s research team

Michigan State University Foundation Professor Guowei Wei wasn’t preparing machine learning techniques for a global health crisis. Still, when one broke out, he and his team were ready to help.
The group already has one machine learning model at work in the pandemic, predicting consequences of mutations to SARS-CoV-2. Now, Wei’s team has deployed another to help drug developers on their most promising leads for attacking one of the virus’ most compelling targets. The researchers shared their intel Oct. 21 in the peer-reviewed journal Chemical Science.
Prior to the pandemic, Wei and his team were already developing machine learning computer models—specifically, models that use what’s known as deep learning—to help save drug developers time and money. The researchers “train” their deep learning models with datasets filled with information about proteins that drug developers want to target with therapeutics. The models can then make predictions about unknown quantities of interest to help guide drug design and testing.
Over the past three years, the Spartans’ models have been among the …

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ElectrifAi Announces Availability of New Machine Learning Models on Google Cloud Marketplace

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TipRanks3 Stocks Flashing Signs of Strong Insider BuyingFor investors, finding the right sign is part of the game. Stocks don’t necessarily pick themselves, and the investors who do pick them need to know that they’re making the right choice. Fortunately for investors – and the safety of their portfolios – there are reliable signals that a stock is worth buying. One of the best is the insider buying.Insiders are corporate officers, deeply invested in their company’s success or failure, they are usually stockholders themselves – but they are responsible for more than just their own portfolios. Corporate officers are beholden to their Boards of Directors, to their fellow company officers, and to the stock owning public to ensure profits and returns on the shares – and so, when these insiders start buying large blocs, investors should take note.TipRanks follows the insiders’ trades, making use of the publicly published stock moves to track them. The Insiders’ Hot Stocks page provides the scoop on which stocks the market’s insiders are buying – or selling – so that you can make informed purchases. We’ve picked three stocks with recent informative buys to show how the data works for you.Agree Realty Corporation ( …

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Dataiku | Envelop Risk Brings Advanced Machine Learning to Cyber Risk With Dataiku

dataiku | envelop risk brings advanced machine learning to cyber risk with dataiku

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Following on from its $6 million Q2 Series A investment, top global insurtech firm delivers AI-driven quantification to cyber risk London, UK – 28th October 2020 – Today, Dataiku, one of the world’s leading Enterprise AI and machine learning platforms, has announced its partnership with global specialty cyber underwriting specialist Envelop Risk. Envelop Risk is working with Dataiku to model and mitigate cyber and other forms of complex, unprotected risk for insurers. Envelop Risk specialises in applying machine learning to complex corporate risks. In May, the firm announced a Series A investment of $6 million, led by AI-specialist investor Alpha Intelligence Capital. Since launch, Envelop Risk has underwritten well over $100 million in premium, placing it among the top insurtech firms globally by this metric. The firm operates as a lead market in cyber reinsurance, in partnership with MS Amlin. Envelop Risk’s founding team and directors combine decades of leadership experience in both insurance and deep tech enterprises such as aerospace and defense. The firm reconceived how machine learning, human expertise, and underwriting can be integrated, using Dataiku’s end-to-end data science platform to build a system that enables more effective risk management by the underwriters of the future.The firm began working with …

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Machine Learning and Artificial Intelligence Partner: Expert.ai – Central Banking

machine learning and artificial intelligence partner: expert.ai – central banking

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Artificial intelligence (AI) is already embedded into many aspects of everyday life and is becoming increasingly popular within the financial industry. AI-driven online platforms can, for example, help consumers manage their money more effectively, as well as make loan applications or insurance claims easier, quicker and more transparent.

However, in recent months, the use of AI and machine learning platforms has become even more crucial for central banks and financial supervisors. With the

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Top JavaScript-Based Machine Learning Frameworks

top javascript-based machine learning frameworks

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While Python and C++ programming languages have become a popular choice when it comes to machine learning framework, JavaScript is not too far behind. Looking around, one may find that JavaScript frameworks have also been implemented in AI. In fact, as per the GitHub review of best machine learning technologies, JavaScript occupies the third position after Python and C++, while R falls in the eighth place. These JavaScript frameworks are boosting business growth with artificial intelligence and machine learning. In this article, in no particular order, we list top JavaScript-based machine learning frameworks.

Brain.js

Brain.js is an open-source, JavaScript-based framework that simplifies the process of defining, training and running neural networks. It can be used with Node.js or at the client-side browser for training machine learning algorithms. This framework is particularly useful for individuals who are just starting out in machine learning and apprehensive to math-heavy technicalities and jargons. 

Brain.js supports several networks such as feed-forward networks, recurrent neural networks, Ellman networks, and long short-term memory networks.

TensorFlow.js

TensorFlow.js is an end-to-end open-source framework maintained by Google. TensorFlow forms the foundation of network software such as DeepDream that can capture, detect, and classify images. …

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Can Machine Learning Predict Atrial Fibrillation Readmissions?

can machine learning predict atrial fibrillation readmissions?

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A recent analysis in Health Services Research and Managerial Epidemiology suggests that machine learning can play a role in helping predict readmissions for atrial fibrillation (AFib).

The authors used data from the 2013 Nationwide Readmissions Database on AFib, aiming to create risk prediction models and ultimately predict 90-day hospital readmission rates. The researchers employed multiple machine learning methods (k-Nearest Neighbors, Decision Tree, and Support Vector Machine) to determine variable importance. The average patient age was 64.9 years, with 62% of patients being male. The primary outcome of interest was 90-day hospital readmissions status. There was a total of 9,468 (weighted n=20,612) cases for 90-day readmissions prior to applying exclusion criteria, and 4,922 cases after applying criteria.
According to the researchers, the 90-day readmission rate was 17.6%. They reported that important variables in 90-day readmission predictions for AFib catheter ablation included patient age, number of diagnoses, and the total number of hospital discharges. According to the results, k-Nearest Neighbor showed the best performance and most accurate predictive ability (85%), followed by Decision Tree. Support Vector Machine, they reported, was not ideal.
“Machine learning methods can produce accurate models in predicting hospital readmissions for patients with AFib,” the authors wrote. “The likelihood of readmission to the hospital increases as …

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NOAA and Google to apply machine learning to satellite data – SpaceNews

noaa and google to apply machine learning to satellite data – spacenews

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SAN FRANCISCO – The National Oceanic and Atmospheric Administration announced an agreement Oct. 27 with Google Cloud to explore the benefits of artificial intelligence and machine learning to enhance the agency’s use of satellite and environmental data.
Under the three-year Other Transaction Authority (OTA) agreement, NOAA’s National Environmental Satellite, Data and Information Service and Google Cloud will conduct pilot projects to explore ways artificial intelligence and machine learning can “amplify NOAA’s environmental monitoring, weather forecasting, climate research and technical innovation,” according to a NOAA news release. NOAA did not disclose the value of the OTA.
“Strengthening NOAA’s data processing through the use of big data, artificial intelligence, machine learning and other advanced analytical approaches is critical for maintaining and enhancing the performance of our systems in support of public safety and the economy,” Acting NOAA Administrator Neil Jacobs said in a statement. “I am excited to utilize new authorities granted to NOAA to pursue cutting-edge technologies that will enhance our mission and better protect lives and property.”
NOAA captures troves of environmental data and imagery from its current constellation of weather satellites and its future constellation is likely to gather far more. NOAA officials often say that one …

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