Multi-step passenger demand forecasting is a crucial task in on-demand vehicle sharing services. However, predicting passenger demand over multiple time horizons is generally challenging due to the nonlinear and dynamic spatial-temporal dependencies. In this work, we propose to model multi-step citywide passenger demand prediction based on a graph and use a hierarchical graph convolutional structure to capture both spatial and temporal correlations simultaneously. Our model consists of three parts: 1) a long-term encoder to encode historical passenger demands; 2) a short-term encoder to derive the next-step prediction for generating multi-step prediction; 3) an attention-based output module to model the dynamic temporal and channel-wise information. Experiments on three real-world datasets show that our model consistently outperforms many baseline methods and state-of-the-art models.

This resource is part of a series on specific topics related to data science: regression, clustering, neural networks, deep learning, decision trees, ensembles, correlation, Python, R, Tensorflow, SVM, data reduction, feature selection, experimental design, cross-validation, model fitting, and many more. To keep receiving these articles, sign up on DSC. To make sure you keep getting these emails, please add [email protected] to your address book or whitelist us.

Stripe uses machine learning to respond to our users' complex, real-world problems. Machine learning powers Radar to block fraud, and Billing to retry failed charges on the network. Stripe serves millions of businesses around the world, and our machine learning infrastructure scores hundreds of millions of predictions across many machine learning models. These models are powered by billions of data points, with hundreds of new models being trained each day. Over time, the volume, quality of data, and number of signals have grown enormously as our models continuously improve in performance.

This course is going to show you a few different ways: including the powerful A2C (Advantage Actor-Critic) algorithm, the DDPG (Deep Deterministic Policy Gradient) algorithm, and evolution strategies. Evolution strategies is a new and fresh take on reinforcement learning, that kind of throws away all the old theory in favor of a more "black box" approach, inspired by biological evolution.

The Future Of Work: Are You Ready For Smart Cobots?

"If intelligence was a cake, unsupervised learning would be the cake, supervised learning would be the icing on the cake, and reinforcement learning would be the cherry on the cake. We know how to make the icing and the cherry, but we don't know how to make the cake." By 2016, Yann LeCun began to hedge with his use of the term "unsupervised learning". In NIPS 2016, he started to call it in even more nebulous terms "predictive learning": I have always had trouble with the use of the term "Unsupervised Learning". In 2017, I had predicted that Unsupervised Learning will not progress much and said "there seems to be a massive conceptual disconnect as to how exactly it should work" and that it was the "dark matter" of machine learning.

There's still much more that we haven't covered yet, such as how to actually train a CNN. Part 2 of this CNN series will do a deep-dive on training a CNN, including deriving gradients and implementing backprop. Subscribe to my newsletter if you want to get an email when Part 2 comes out (soon)! If you're eager to see a trained CNN in action: this example Keras CNN trained on MNIST achieves 99.25% accuracy.

Researchers from Samsung's AI Centre located in Moscow have created a new system that can transform still facial images into video sequences of the human face making speech expressions. According to the study, the system creates realistic virtual talking heads through applying the facial landmarks of a target face onto a source face -- for example, a still photo -- to allow the target face to control how the source face moves. "Such ability has practical applications for telepresence, including videoconferencing and multi-player games, as well as [the] special effects industry," Samsung said. While the existence of "deepfake" technology isn't something new, Samsung's new system does not use 3D modelling and only requires one photograph to create a face model. If the system is able to use 32 images to create a model, the system will be able to "achieve [a] perfect realism and personalisation score," Samsung said.

Deep learning is increasingly capable of assessing the emotion of human faces, looking across an image to estimate how happy or sad the people in it appear to be. What if this could be applied to television news, estimating the average emotion of all of the human faces seen on the news over the course of a week? While AI-based facial sentiment assessment is still very much an active area of research, an experiment using Google's cloud AI to analyze a week's worth of television news coverage from the Internet Archive's Television News Archive demonstrates that even within the limitations of today's tools, there is a lot of visual sentiment in television news. To better understand the facial emotion of television, CNN, MSNBC and Fox News and the morning and evening broadcasts of San Francisco affiliates KGO (ABC), KPIX (CBS), KNTV (NBC) and KQED (PBS) from April 15 to April 22, 2019, totaling 812 hours of television news, were analyzed using Google's Vision AI image understanding API with all of its features enabled, including facial detection. Facial detection is very different from facial recognition. It only counts that a human face is present in an image, it does not actually attempt to discern who that person is.

We are in the midst of a deep learning revolution. Unprecedented success is being achieved in designing deep neural network models for building computer vision and Natural Language Processing (NLP) applications. State-of-the-art benchmarks are disrupted and updated on a regular basis in tasks like object detection, language translation, and sentiment analysis. It's a great time to work with deep learning! The application of deep learning in Information Security (InfoSec) is still very much in its nascent stages.