The company sells Medicare Advantage plans, focusing on customer experience and leveraging machine learning and artificial intelligence to …

There has been some discussion in this sub, but what are some external blogs/resources that have good best practices on productionizing ML and DL models and building out infrastructure to build, test, and deploy ML models, particularly at a small company? A sort of How-To guide would be great!

Hello everyone, I recently noticed that the use of TFRecords isn't as popular as it should be. The efficiency and advantages that it provides, including but not limited to easy integration into TPU pipelines for Tensorflow, led me to creating a repository full of command line scripts to convert data from all popular domains like Audio, Text and Images (with video support coming soon) to TFRecord formats. The scripts also contain support for automatic SQLite and CSV datatype parsing and buffering. Along with this, they support an option for multiprocessing and are made with minimization of memory footprint in mind. If you are interested then please check out the repository here, give it a star if it is helpful and let me know if you have any feedback or suggestion.

It's still quite useful, but it does not do any kind of feature selection nor does it consider any kind of feature importance when making predictions. All it does to make predictions is to calculate the distance in feature space between the new observation you wish to make a prediction for and all the other observations it has been trained on, and find the k closest old ones to the new one, then take some aggregate of the target variable of the k closest observations (usually mean for regression and mode for classification). If you add several completely random columns to your data, kNN will use them in calculating the distance to the exact same extent as the meaningful columns. This is opposed to smarter algorithms like linear models that can figure out to ignore features that contain no predictive value. If your model is getting worse when you add new features, it doesn't even mean they contain no value.

Artificial intelligence – AI – was a mere computational theory back in the 1950s when Alan Turing designed the first Turing Test to measure a …

The common goal of investors is how to achieve a stable asset growth.

Description Here's a basic definition of machine learning: "Algorithms that parse data, learn from that data, and then apply what they've learned to make informed decisions" An easy example of a machine learning algorithm is an on-demand music streaming service. For the service to make a decision about which new songs or artists to recommend to a listener, machine learning algorithms associate the listener's preferences with other listeners who have a similar musical taste. This technique, which is often simply touted as AI, is used in many services that offer automated recommendations. Machine learning fuels all sorts of automated tasks that span across multiple industries, from data security firms that hunt down malware to finance professionals who want alerts for favorable trades. The AI algorithms are programmed to constantly be learning in a way that simulates as a virtual personal assistant--something that they do quite well.

In May 2015, The Simpsons voice actor Harry Shearer – who plays a number of key characters including, quite incredibly, both Mr Burns and Waylon Smithers – announced that he was leaving the show. By then, the animated series had been running for more than 25 years, and the pay of its vocal cast had risen from $30,000 an episode in 1998 to $400,000 an episode from 2008 onwards. But Fox, the producer of The Simpsons, was looking to cut costs – and was threatening to cancel the series unless the voice actors took a 30 per cent pay cut. Most of them agreed, but Shearer (who had been critical of the show's declining quality) refused to sign – after more than two decades, he wanted to break out of the golden handcuffs, and win back the freedom and the time to pursue his own work. Showrunner Al Jean said Shearer's iconic characters – who also include Principal Skinner, Ned Flanders and Otto Mann – would be recast.

Artificial intelligence can now gauge human emotions, and it's being used in everything from education to marketing, experts say. Your emotions could potentially be tracked using your Wi-Fi router and analyzed by AI, according to a new study from London's Queen Mary University. Researchers used radio waves like those used in Wi-Fi to measure heart and breathing rate signals, which could determine how a person is feeling. The study shows just how pervasive emotion-monitoring could become. "In education, AI could be used in adapting content to serve the needs of each child best," Kamilė Jokubaitė, CEO and founder of Attention Insight, who was not involved in the study, said in an email interview.

We introduce the task of changing the narrative point of view, where characters are assigned a narrative perspective that is different from the one originally used by the writer. The resulting shift in the narrative point of view alters the reading experience and can be used as a tool in fiction writing or to generate types of text ranging from educational to self-help and self-diagnosis. We introduce a benchmark dataset containing a wide range of types of narratives annotated with changes in point of view from deictic (first or second person) to anaphoric (third person) and describe a pipeline for processing raw text that relies on a neural architecture for mention selection. Evaluations on the new benchmark dataset show that the proposed architecture substantially outperforms the baselines by generating mentions that are less ambiguous and more natural.