The practice of data science requires the use algorithms and data science methods to help data professionals extract insights and value from data. A recent survey by Kaggle revealed that data professionals used data visualization, logistic regression, cross-validation and decision trees more than other data science methods in 2017. Looking ahead to 2018, data professionals are most interested in learning deep learning (41%). Kaggle conducted a survey in August 2017 of over 16,000 data professionals (2017 State of Data Science and Machine Learning). Their survey included a variety of questions about data science, machine learning, education and more.

At Uber, event forecasting enables us to future-proof our services based on anticipated user demand. The goal is to accurately predict where, when, and how many ride requests Uber will receive at any given time. Extreme events--peak travel times such as holidays, concerts, inclement weather, and sporting events--only heighten the importance of forecasting for operations planning. Calculating demand time series forecasting during extreme events is a critical component of anomaly detection, optimal resource allocation, and budgeting. Although extreme event forecasting is a crucial piece of Uber operations, data sparsity makes accurate prediction challenging.

Artificial intelligence is years, even decades, from replicating functions of the human mind, but it's still getting serious work done today. And its influence will only expand. The irony of all that promise: Human minds are way behind. Relatively few have a baseline understanding about how AI and deep learning truly work. Techniques like machine learning, which underpin many of today's AI tools, aren't easy to grasp.

The practice of data science requires the use algorithms and data science methods to help data professionals extract insights and value from data. A recent survey by Kaggle revealed that data professionals used data visualization, logistic regression, cross-validation and decision trees more than other data science methods in 2017. Looking ahead to 2018, data professionals are most interested in learning deep learning (41%). Kaggle conducted a survey in August 2017 of over 16,000 data professionals (2017 State of Data Science and Machine Learning). Their survey included a variety of questions about data science, machine learning, education and more.

Deep neural networks and Deep Learning are powerful and popular algorithms. And a lot of their success lays in the careful design of the neural network architecture. I wanted to revisit the history of neural network design in the last few years and in the context of Deep Learning. For a more in-depth analysis and comparison of all the networks reported here, please see our recent article. Reporting top-1 one-crop accuracy versus amount of operations required for a single forward pass in multiple popular neural network architectures. It is the year 1994, and this is one of the very first convolutional neural networks, and what propelled the field of Deep Learning. This pioneering work by Yann LeCun was named LeNet5 after many previous successful iterations since the year 1988!

While artificial intelligence applications in business and industry remain limited to narrow machine learning tasks, we are seeing progressive improvements in the convergence of algorithms and hardware that will have significant implications for how well and how quickly we can implement AI. Researchers can now train neural networks within a few hours or days, which opens up an amazing range of possibilities, products, and things to learn -- as well as challenges -- that we could not have even considered before. For example, Google's AI group, DeepMind, is hard at work unraveling the mysteries of how proteins fold themselves, a discovery that could have far-reaching implications for health care. It is also very much involved with the research community in working through the ethical issues of AI. As I see it, 2018 will be the year AI will meet a crossroads -- when companies are better able to skim the hype from the reality, and when we can apply AI for both the good and the bane of humanity.

Connecting the dots for a Deep Learning App … Our day to day activities is filled with Emotions and Sentiments. Ever wondered how we can identify these sentiments through computers? Oops, computers who have no brains:)? Try this Deep Learning App yourself (refresh a couple of times initially if there's Application Error): Dot 0: Deep Learning in Sentiment Analysis Sentiment analysis is a powerful application which extends its arms to the following fields in the modern day world. According to Wikipedia: Sentiment analysis (sometimes known as opinion mining or emotion AI) refers to the use of natural language processing, text analysis, computational linguistics, and biometrics to systematically identify, extract, quantify, and study affective states and subjective information. On this note, let's develop a Movie Review Sentiment Application using the methods of Deep Learning.

Atlanta's traffic congestion infamy -- the city regularly makes every annual top list due to its bottlenecks -- is partially caused by car accidents at large, busy intersections. After a few accidents occur, the Georgia Department of Transportation (GDOT) may swoop in to assess the situation to make the intersection safer. But what if they could do that before the accidents happen? "Most crashes are preventable, which is the concept behind the'Vision Zero' Initiative," says Dr. Jidong Yang, assistant professor of civil engineering and director of Kennesaw State's Georgia Pavement and Traffic Research Center. Vision Zero was originally created in Sweden in the 1990s in order to streamline mobility within cities while eliminating traffic fatalities and severe injuries.

Using artificial intelligence to predict when patients may die sounds like an episode from the dystopian science fiction TV series "Black Mirror." But Stanford University researchers see this use of AI as a benign opportunity to help prompt physicians and patients to have necessary end-of-life conversations earlier. Many physicians often provide overly rosy estimates about when their patients will die and delay having the difficult conversations about end-of-life options. That understandable human tendency can lead to patients receiving unwanted, expensive and aggressive treatments in a hospital at their time of death instead of being allowed to die more peacefully in relative comfort. The alternative being tested by a Stanford University team would use AI to help physicians screen for newly-admitted patients who could benefit from talking about palliative care choices.

Multilayered artificial neural networks are becoming a pervasive tool in a host of application fields. At the heart of this deep learning revolution are familiar concepts from applied and computational mathematics; notably, in calculus, approximation theory, optimization and linear algebra. This article provides a very brief introduction to the basic ideas that underlie deep learning from an applied mathematics perspective. Our target audience includes postgraduate and final year undergraduate students in mathematics who are keen to learn about the area. The article may also be useful for instructors in mathematics who wish to enliven their classes with references to the application of deep learning techniques. We focus on three fundamental questions: what is a deep neural network? how is a network trained? what is the stochastic gradient method? We illustrate the ideas with a short MATLAB code that sets up and trains a network. We also show the use of state-of-the art software on a large scale image classification problem. We finish with references to the current literature.