Lifeboat Foundation

Modern machine learning applications need to process a humongous amount of data and generate multiple features. Python’s datatable module was created to address this issue. It is a toolkit for performing big data (up to 100GB) operations on a single-node machine, at the maximum possible speed.

• Fraud detection techniques mostly stem from the anomaly detection branch of data science.
• If the dataset has sufficient number of fraud examples, supervised machine learning algorithms for classification like random forest, logistic regression can be used for fraud detection.
• If the dataset has no fraud examples, we can use either the outlier detection approach using isolation forest technique or anomaly detection using the neural autoencoder.
• After the machine learning model has been trained, it’s evaluated on the test set using metrics such as sensitivity and specificity, or Cohen’s Kappa.

With global credit card fraud loss on the rise, it is important for banks, as well as e-commerce companies, to be able to detect fraudulent transactions (before they are completed).

According to the Nilson Report, a publication covering the card and mobile payment industry, global card fraud losses amounted to $22.8 billion in 2016, an increase of 4.4% over 2015. This confirms the importance of the early detection of fraud in credit card transactions.

Several companies, like SignAll and Kintrans, have created hand-tracking software that tries, with little success so far, to allow the millions of people that use sign language and an app to easily communicate with anyone.

Now, a new hand-tracking algorithm from Google’s AI labs might be a big step in making this ambitious software everything it originally promised.

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Aren Jay shared this cogent article to my Timeline… It is not new even Hippocrates was able to determine that the gut causes and or assists in all diseases. But the 19th and 20th centuries researchers began saying that microbes are good for mankind which sent science reeling through generations until this day… Respect r.p.berry & AEWR wherein we have developed a formula and Algorithm that deals with this very serious problem completely. A very expensive cure but one that will take Woman-Man past the Escape Velocity so many have written about…

Down the road

The end game for quantum computing is a fully functional, universal fault-tolerant gate computer. To fulfill its promise, it needs thousands, maybe even millions, of qubits that can run arbitrary quantum algorithms and solve extremely complex problems and simulations.

Before we can build a quantum machine like that, we have a lot of development work to be done. In general terms, we need:

Find out more: http://theory-of-thought.com/blog/approximations-foundation-hidden-framework/

Hotel revenue management and use of analytics for room sales has remained largely unchanged for decades since the early 1980s when hotels started looking at yield and how they could optimize the revenue each room could generate. By the mid-1990’s, Marriott’s successful execution of revenue management strategies were adding between $150 — $200 million in annual revenue and thus marked the beginning of data intelligence to drive new revenue.

Fast forward to 2016 — and the part insight, part intuition, part data-driven approach to revenue management largely hasn’t moved into the new age of big data for most hoteliers.

There is a new application of data modelling hotels are utilizing to see big gains in RevPAR (Revenue Per Available Room) and this comes through price differentiation. That is — dynamically displaying different room rates for every person that views your hotel search price query.

This presentation was posted by Jason Mayes, senior creative engineer at Google, and was shared by many data scientists on social networks. Chances are that you might have seen it already. Below are a few of the slides. The presentation provides a list of machine learning algorithms and applications, in very simple words. It also explain the differences between AI, ML and DL (deep learning.)

The groundwork for machine learning was laid down in the middle of last century. But increasingly powerful computers – harnessed to algorithms refined over the past decade – are driving an explosion of applications in everything from medical physics to materials, as Marric Stephens discovers.