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.

TensorFlow is an open source software library created by Google that is used to implement machine learning and deep learning systems. These two names contain a series of powerful algorithms that share a common challenge--to allow a computer to learn how to automatically spot complex patterns and/or to make best possible decisions. If you're interested in details about these systems, you can learn more from the Toptal blog posts on machine learning and deep learning. TensorFlow, at its heart, is a library for dataflow programming. It leverages various optimization techniques to make the calculation of mathematical expressions easier and more performant.

Earlier this year a group of world experts convened to discuss Doomsday scenarios and ways to counter them. The problem though was that they found discussing the threats humanity faces easy, but as for solutions, well, in the majority of cases they were stumped. This week DeepMind, Google's world famous Artificial Intelligence (AI) arm, in a world first, announced they have an answer to the potential AI apocalypse predicted by the group and leading luminaries ranging from Elon Musk to Stephen Hawking, whose fears of a world dominated by AI powered "killer robots" have been hitting the headlines all year, in the form of a test that can assess how dangerous AI's and algorithms really are, or, more importantly, could become. In the announcement, which was also followed up by a paper on the topic, DeepMind said they'd managed to develop a test that would help people assess the safety of new AI algorithms that will power everything from self-driving cars, and cancer treatments to biometric security solutions and voice recognition, as well as those infamous autonomous robots and autonomous weapons systems, and DeepMind's lead researcher, Jan Leike, said that AI algorithms that don't pass their test are probably "pretty dangerous." The test in question is a series of 2D video games in a chessboard like plane made out of pixel blocks that the researchers call "GridWorld" that puts AI's through a series of games in order to evaluate nine safety features that, when combined, can then be used to determine how dangerous an AI is, whether it can modify itself and if it can cheat the game.

About one in seven U.S. adults has diabetes now, according to the Centers for Disease Control and Prevention. With this in mind, this is what we are going to do today: Learning how to use Machine Learning to help us predict Diabetes. The diabetes data set was originated from UCI Machine Learning Repository and can be downloaded from here. "Outcome" is the feature we are going to predict, 0 means No diabetes, 1 means diabetes. The k-NN algorithm is arguably the simplest machine learning algorithm.

Currently, the way AI assistants generate'lifelike' responses are based off fed data. Machines do not think for themselves. Using deep learning, multiple layers within the program powering AI assistants are automatically tweaked and improved based off of the enormous quantities of data it has been fed. For example, AlphaGo analysed millions of games of Go, and played itself thousands of times to understand which strategies and moves it could use to successfully win a game. It's the same with iFlytek's trial in Anhui Provincial Hospital, AI receptionists will be fed data regarding symptoms and possible diagnoses, and rely on this knowledge to direct its patients, the human language is very different.

Sequence prediction problems have been around for a long time. They are considered as one of the hardest problems to solve in the data science industry. These include a wide range of problems; from predicting sales to finding patterns in stock markets' data, from understanding movie plots to recognizing your way of speech, from language translations to predicting your next word on your iPhone's keyboard. With the recent breakthroughs that have been happening in data science, it is found that for almost all of these sequence prediction problems, Long short Term Memory networks, a.k.a LSTMs have been observed as the most effective solution. LSTMs have an edge over conventional feed-forward neural networks and RNN in many ways.

Deep learning enthusiam is based on applied researches of a huge community, coming from both the academic and industrial side. In this ecosystem, 3 researchers has become very popular: Yoshua Bengio (McGill University), Geoffrey Hinton (Google) and Yann Le Cun (Facebook). These three authors has co-published in the journal Nature a review on deep learning (part of a "Nature Insight" supplement on Machine Intelligence, with another interesting paper by Michael Littman about Reinforcement Learning). These increasing attention about deep learning make Neural networks, and Convolutional Neural Nets very popular and manipulated by a huge community of coders. Test and learn engineering has been experimented in several places of the world, ranging from Toronto, New-York, Paris or San Francisco.

Last week at the Amazon Web Services' re:Invent conference, AWS and Intel introduced a new video camera, AWS DeepLens, that acts as an intelligent device that can run deep learning algorithms on captured images in real-time. The key difference between DeepLens and any other AI-powered camera lies in the horsepower that makes it possible to run machine learning inference models locally without ever sending the video frames to the cloud. Developers and non-developers rushed to attend the AWS workshop on DeepLens to walk away with a device. There, they were enticed with a hot dog to perform the infamous "Hot Dog OR Not Hot Dog" experiment. I managed to attend one of the repeat sessions, and carefully ferried the device back home.

This post includes 5 specific video-based options for doing just that, collectively consisting of many, many hours of insights. If you already possess some basic knowledge of neural networks, it may be time to jump in and tackle some more advanced concepts. Machine learning and (more recently) deep learning summer schools are held in numerous locations every year. Given the rising prominence of Montreal's deep learning and AI ecosystem, and the superstar speakers that are readily available locally and affiliated with local institutions further flung, here are Montreal's Deep Learning Summer School video playlists (and slides) from the past 2 years. You'll find most of the big name speakers within, who cover a wide range of topics.

NVIDIA Deep Learning Institute, together with SGInnovate, is hosting the practical Deep Learning Fundamentals training. In this full-day workshop, you will start with the basic concepts of deep learning and quickly move to learning how to solve real-word problems using deep learning. NVIDIA Deep Learning Institute Certified Instructors will blend lecture and hands-on, real-world exercises to explore how to solve the most challenging problems with deep learning. You will receive a Beginner Level certificate from Deep Learning Institute! Aik Beng, Ng is a Senior Solutions Architect, Deep Learning at NVIDIA APAC.