Whether „Driving Home for Christmas", „Winter Wonderland", „Let it snow!" or „Last Christmas" – every year christmas songs are taking over the charts again. While average Joe is joyfully putting on the next christmas song, the data scientist starts his journey of discovery through the snowy music history. The data set comes from 55000 Song Lyrics, which contains over 55,000 songs. Our goal is to perform a comprehensive analysis of the song texts to identify the Christmas songs. In order to do so, first we add an additional column to the data frame to give each song a label of either Christmas or Not Christmas, where every song which contains the words Christmas, Xmas or X-mas will be labeled as Christmas and otherwise as Not Christmas. This is just the initialization of the labels, later we will apply Naive Bayes to a training set to identify the other Christmas songs.

I am interested in knowing if there are any publications that propose models where the output is not a softmax but a vector in the embedding space or, alternatively, suggestions on how to do it, like what loss function to use (e.g. MSE between output and expected embedded vectors) or not to use, and why. I have not been able to find anything in google or google scholar, that's why I resort to the knowledge of this subreddit. I would appreciate any help.

Fifteen years ago, there was a gap between digital journalists and non-digital journalists. In 2018 and the coming years, there will be another gap: between journalists that can speak to artificial intelligence and journalists who can't. In a media landscape driven by algorithms that filter the news, by robots that can write some pieces of journalism, and by machines that can automatically translate any post, speaking fluently to AI could make the difference for a journalist. It's not new for technology to be a determining factor for this profession's evolution. It began with the rotary press, then with radio frequencies, followed by computer science, then by the network and the always-on connection, and now the omnipresence of data. Each innovation changed the way we discover news, so they also changed the way we produce news.

AI, deep learning and neural networks have brought our society to a point of progress that was once unimaginable. It's difficult to overstate the speed of technological progress, and equally difficult to comprehend the extent of its sophistication and efficiency. In a single day, we now process as much data as we did in a month only a decade ago. With a revolution unfolding at such a breakneck pace, questions have naturally arisen as to how technology, especially artificial intelligence (AI), will affect the workplace – and our way of life. If it can impact everyone from taxi drivers to attorneys, what sort of world will we see, even in just a few years?

A neural network passes an input vector through a series of "matrix (rotations / scaling / translation) operations followed by a non-linearity". The output vector of the neural network may or may not have the same norm as the input vector. Could you please point me to a / some neural network architecture/s that is / are able to preserve the norm of the input vector? If we consider the norm as a measure of the energy of the input vector / signal, what I am looking for is a neural net that can preserve the energy of the input signal. Is there any other metric that is analogous to the energy of the input signal?

"Star Wars: The Last Jedi" has been a box office success, racking up more than $220 million during its opening weekend, and is a hit with critics but its audience score on Rotten Tomatoes is barely floating above 50 percent as the result of what appears to be an organized review bombing campaign. Of the more than 116,000 audience reviews "Star Wars: The Last Jedi" has received so far, just 55 percent have been positive. Many of the negative reviews take issue for what the viewers perceive to be too political and pushing a "social justice warrior" agenda. It's not often that a film was well received by critics as "Star Wars: The Last Jedi" struggles to please fans. While the franchise has a giant and rabid fan base, it appears the audience review section has been invaded by the only group more dedicated than Star Wars diehards: trolls.

"What we're doing with AI is making it possible for anybody to enjoy the experience," says 52-year-old King, who lost his sight in college due to a degenerative eye disease and now works at Facebook as an accessibility specialist. In addition to the improved facial recognition, Facebook has in recent years also automated descriptions of what's happening in a photo.

Artificial intelligence has been around for a while and it is definitely here to stay. Everyday experiences with Siri, Google Assistant and Amazon Alexa are all powered by AI. In 2016, Deloitte Global estimated that 2.5 trillion pictures would be shared or stored online. If you want to search for a certain picture you took one night while eating pizza, AI or Google can help show a filtered result of all photos of you having pizza. In short, we have to accept the fact that AI is here to aid human intelligence and make our lives better. Now let's focus on marketing: AI influences analytics, social media communications, outbound marketing and even content marketing.

Artificial Intelligence We are living in exciting and innovative times with futuristic technology literally at our fingertips. But for the longest time, small to medium sized businesses were not serviced by the latest tech trends enterprises have been able to benefit from. In this article, we'll explore these technology trends and how they will impact business in 2018 and beyond. So, what kind of things can this'smart' tech do? Just 4 months ago, an AI machine managed to complete a University level math exam 12 times faster than it normally takes the average human. How? Through the art of machine learning; where computers learn and adapt through experience without explicitly being programmed. Furthermore, Facebook made headlines earlier this year when their chatbots created their own language. Some Fake News stories say that the engineer's pulled the plug in a panic after they were getting too smart.

I can't seem to find a CIFAR version with pre-deactivated labels for the unlabeled samples. I suppose if you use it as a semi-supervised benchmark you always retain the same labeled records, don't you? I would run the graph based methods I wrote my master thesis on against it for fun, but I'm afraid my personal workstation hasn't got enough ram for 60000x60000 matrices.