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Optimization tips and tricks on Azure SQL Server for Machine Learning Services

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By using memory-optimized tables, resume features are stored in main memory and disk IO could be significantly reduced. If the database engine server detects more than 8 physical cores per NUMA node or socket, it will automatically create soft-NUMA nodes that ideally contain 8 cores. We then further created 4 SQL resource pools and 4 external resource pools [7] to specify the CPU affinity of using the same set of CPUs in each node. We can create resource governance for R services on SQL Server [8] by routing those scoring batches into different workload groups (Figure.


Machine Learning As Prescriptive Analytics (IT Best Kept Secret Is Optimization)

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We can use machine learning techniques to build a model that predict future demand (future sales levels) for a retail store chain. We can then use optimization to compute optimal inventory management for these stores, making sure cost of inventory and risk of out of shelves are kept at minimum. For instance, that person will model all the inventory constraints (available space, cost per stored item, shelf life, cost of transportation, etc), as well as the business objective (combination of inventory cost and risk of lost sales because of out of shelf) Then, on a regular basis, this model is combined with a particular instance of the business problem to yield an optimization problem (for instance, inventory left from previous week, and predicted demand for the week). Good machine learning papers use good optimization techniques and bad machine learning papers (most of them in fact) use bad out of date ad-hoc optimization techniques.


For the Golden State Warriors, Brain-Zapping Could Provide an Edge

The New Yorker

Though you couldn't tell from the picture, these particular headphones incorporated a miniature fakir's bed of soft plastic spikes above each ear, pressing gently into the skull and delivering pulses of electric current to the brain. Made by a Silicon Valley startup called Halo Neuroscience, the headphones promise to "accelerate gains in strength, explosiveness, and dexterity" through a proprietary technique called neuropriming. "Thanks to @HaloNeuro for letting me and my teammates try these out!" McAdoo tweeted. On Thursday night, McAdoo and his teammates will seek the eighty-ninth and final win of their record-breaking season, as they defend their National Basketball Association title in Game 6 of the final series against LeBron James's Cleveland Cavaliers. The headphones' apparent results, in other words, have been impressive.


Machine Learning As Prescriptive Analytics (IT Best Kept Secret Is Optimization)

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I said, and I wrote, that machine learning and predictive analytics were almost the same. Of course, I also put optimization as the queen of all analytics technologies as it yields best business value. What else would you expect from someone who spent nearly 3 decades in working in optimization? No wonder this view became popular in the optimization community... First, let me reassure readers about my mental health: I still think that optimization is best for computing optimal decisions. I started thinking there was an issue when I met customers willing to use machine learning to solve all the business problems they have.


Machine Learning As Prescriptive Analytics (IT Best Kept Secret Is Optimization)

#artificialintelligence

I said, and I wrote, that machine learning and predictive analytics were almost the same. Of course, I also put optimization as the queen of all analytics technologies as it yields best business value. What else would you expect from someone who spent nearly 3 decades in working in optimization? No wonder this view became popular in the optimization community... First, let me reassure readers about my mental health: I still think that optimization is best for computing optimal decisions. I started thinking there was an issue when I met customers willing to use machine learning to solve all the business problems they have.


Text Analysis 101; A Basic Understanding for Business Users: Words, Entities and Concepts

@machinelearnbot

This blog was originally published on our Text Analysis blog, the blog post aimed to explain how Text Analysis and Natural Language Processing works from a non-technical point of view. For the first installment, we looked at how text is understood by machines, what methods are used in text analysis and why Entity and Concept extraction techniques are so important in the process. Text Analysis refers to the process of retrieving high-quality information from text. It involves Information Retrieval (IR) processes and lexical analysis techniques to study word frequency, distributions, patterns and utilizes information extraction and association analysis to attempt to understand text. The main goal of Text Analysis as a practice is to turn text into data for further analysis, whether that is from a business intelligence, research, data analytics or investigative perspective.


2025: Artificial Intelligence and the recruiting revolution

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We then have to look at the future to find some comfort and try to understand if the big wave of innovation we see today (artificial intelligence, virtual and augmented reality, wearables, big data, Internet of Things etc…) will impact positively on recruiting. On top of these benefits, it should also add a bit of quality: not everybody who says they want a project manager or an IT lead or a financial analyst means exactly the same thing, an artificial intelligence will navigate through business titles giving them a real meaning thanks to data clustering techniques; at the same time, it will be able to score and sort resumes without any individual bias. People data analytics: we have big data and we adopt analytics widely, it makes sense this trend will impact recruiting as well. Real time monitoring of performance and behavior: if a candidate can influence his digital identity, the most important information for any company will anyway come from his / her past performance and behavior.


Is machine learning smart enough to help industry?

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How will these technologies feed machine learning and allow machine design, control systems, production, maintenance and business practices to improve? "Machine learning will help machine builders, integrators and end users by allowing the machines to solve the problems that typically can only be done by humans and, in some cases, can't even be done by humans," says Matt Wicks, vice president, product development, manufacturing systems for Intelligrated, a provider of automated, intelligent conveyance and robotic handling systems in Mason, Ohio. "One thing you might want to take a look at is the Google Trends comparing the search volumes for machine learning vs. artificial intelligence vs. neural networks vs. late-comer deep learning," notes Michael Risse, vice president and CMO at Seeq. "There might be other terms to consider--prescriptive analytics, for example--and then there are the process-industry-specific analytics tools such as advanced process control (APC), statistical process control (SPC), multivariate analysis and even application performance management (APM)," says Risse.


Overfitting and Underfitting With Machine Learning Algorithms - Machine Learning Mastery

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In machine learning we describe the learning of the target function from training data as inductive learning. Generalization refers to how well the concepts learned by a machine learning model apply to specific examples not seen by the model when it was learning. This is good terminology to use in machine learning, because supervised machine learning algorithms seek to approximate the unknown underlying mapping function for the output variables given the input variables. After you have selected and tuned your machine learning algorithms on your training dataset you can evaluate the learned models on the validation dataset to get a final objective idea of how the models might perform on unseen data.


Overfitting and Underfitting With Machine Learning Algorithms - Machine Learning Mastery

#artificialintelligence

In machine learning we describe the learning of the target function from training data as inductive learning. Generalization refers to how well the concepts learned by a machine learning model apply to specific examples not seen by the model when it was learning. This is good terminology to use in machine learning, because supervised machine learning algorithms seek to approximate the unknown underlying mapping function for the output variables given the input variables. After you have selected and tuned your machine learning algorithms on your training dataset you can evaluate the learned models on the validation dataset to get a final objective idea of how the models might perform on unseen data.