Tesla CEO Elon Musk has received a lot of praise lately with advancements of his SpaceX projects along with his Boring Company tunnel machine, but a new report could cloud his reputation. According to the Guardian, worker conditions at Tesla factories are so bad people have reportedly passed out to meet production demands, while others received life-changing injuries. Workers experiencing fainting spells, dizziness, seizures, abnormal breathing and chest pains led to more than 100 calls to ambulances since 2014, while hundreds more were called for injuries and other cases, incident records obtained by the news outlet show. Read: Electric Sled, Earthquakes and Snails: Here Are New Details About Elon Musk's Tunnel Boring Company Pressure on employees, who work alongside giant red robots, shows the lengths Tesla is going to in order to meet CEO Elon Musk's aggressive production goals. "I've seen people pass out, hit the floor like a pancake and smash their face open," Jonathan Galescu, a production technician at Tesla, told the Guardian.

TensorFlow is Google's machine learning framework. In this talk, you will learn how to use TensorFlow effectively. TensorFlow offers high level interfaces like Keras and Estimators, which can be used without being an expert. This talk will show how to implement complex machine learning models and deploy them on any platform that supports TensorFlow. See all the talks from Google I/O '17 here: https://goo.gl/D0D4VE

No one knows what the future holds, but many people are unable to cope with the uncertainty. However, researchers have discovered that the fear of the unknown may be linked to an unusual enlargement of a brain region that is responsible for decision making and motor control. The team believes the findings could help specialists predict those at risk of developing anxiety disorder or OCD later in life, allowing intervention to occur before symptoms arise. Researchers at Dartmouth College conducted MRI scans on 61 students following a survey that measured their ability to tolerate the uncertainty of future negative events. The team analyzed the scans and compared them with the intolerance of uncertainty scores, which showed the size of the striatum was linked with intolerance of uncertainty.

Music streaming service Spotify on Wednesday disclosed it has acquired the team and technology behind Niland, a French start-up with a service for delivering music recommendations. The move signals that Spotify wants to incorporate more artificial intelligence (AI) into its system as it fights off competition from alternatives like Apple Music. Niland is not well-known in the field of AI. But for years its CEO, Damien Tardieu, has done research on ways to extract meaningful information from raw music content in order to form connections with other music. This approach differs from collaborative filtering, one of the techniques that Spotify and others use.

The idea of cross-validation is to split the data into N subsets, to put one subset aside, to estimate parameters of the model from the remaining N-1 subsets, and to use the retained subset to estimate the error of the model. Such a process is repeated N times - with each of the N subsets being used as the validation set . Then the values of the errors obtained in such N steps are combined to provide the final estimate of the model error. The cross-validation is used in various classification and prediction procedures, such as regression analysis, discriminant analysis, neural networks and classification and regression trees (CART) . The goal is to improve the quality of the decision that is made from the outcome of the study on the basis of statistical methods, and to ensure that maximum information is obtained from scarce experimental data.

While this will serve as an overview of Scott's essay, which you can read for further detail and mathematical insights, we will start by with Fortmann-Roe's verbatim definitions which are central to the piece: Error due to Bias: The error due to bias is taken as the difference between the expected (or average) prediction of our model and the correct value which we are trying to predict. Again, imagine you can repeat the entire model building process multiple times. Fortmann-Roe ends the section on over- and under-fitting by pointing to another of his great essays (Accurately Measuring Model Prediction Error), and then moving on to the highly-agreeable recommendation that "resampling based measures such as cross-validation should be preferred over theoretical measures such as Aikake's Information Criteria." I recommend reading Scott Fortmann-Roe's entire bias-variance tradeoff essay, as well as his piece on measuring model prediction error.

Note that, while there are numerous machine learning ebooks available for free online, including many which are very well-known, I have opted to move past these "regulars" and seek out lesser-known and more niche options for readers. The book has wide coverage of probabilistic machine learning, including discrete graphical models, Markov decision processes, latent variable models, Gaussian process, stochastic and deterministic inference, among others. The material is excellent for advanced undergraduate or introductory graduate course in graphical models, or probabilistic machine learning. One of these target audiences is university students(undergraduate or graduate) learning about machine learning, including those who are beginning a career in deep learning and artificial intelligence research.

Which authors of this paper are endorsers? Disable MathJax (What is MathJax?)

In this tutorial we look at the word2vec model by Mikolov et al. This model is used for learning vector representations of words, called "word embeddings". This tutorial is meant to highlight the interesting, substantive parts of building a word2vec model in TensorFlow. This basic example contains the code needed to download some data, train on it a bit and visualize the result. But first, let's look at why we would want to learn word embeddings in the first place.

In the 2001 blockbuster, 'What Women Want', Mel Gibson's macho character is in an accident that allows him to hear what women are thinking. And when it comes to online dating, researchers suggest that men would be wise to follow suit and get into the mind-set of women – albeit without going through an accident. A new study has found that men are more likely to be successful in online dating if they create profiles that are more like the women they hope to attract. The researchers looked at three months' worth of anonymous data from a popular dating site, including the profiles and clickstreams of 410,000 active users from 10 metropolitan areas. Together, these users wrote 25 million messages, generated 286 million clicks on the site and rated other users' profiles 864 million times.