There was a time, not too long ago, when Wal-Mart was the undisputed king of retail. However, e-commerce has changed the landscape, and over the last few years, Amazon.com, Inc. has been taking an increasingly large slice of the retail pie. Wal-Mart has been playing catch-up in online sales, but now it seems the retail giant is ready to take the fight to Amazon, with a little help from NVIDIA Corporation and artificial intelligence (AI). In a note to clients, Global Equities Research analyst Trip Chowdhry revealed that Wal-Mart would be building huge data centers to house its cloud computing and make a sizable push into deep learning, a segment of artificial intelligence.

If you are practitioner interested in deep learning for NLP, you may have different goals and requirements from the material. For example, you may want to focus on the methods and applications rather than the foundational theory. The course is comprised of 13 lectures, although the first and second lectures are both split into two parts. The complete lecture breakdown is provided below. The GitHub repository for the course provides links to slides, flash videos and reading for each lecture. I would recommend watching the videos via this unofficial YouTube playlist. Below is a course overview slide taken from the first lecture.

Key themes included the polling failures in 2016 US Elections, Deep Learning, IoT, greater focus on value and ROI, and increasing adoption of predictive analytics by the "masses" of industry.

Join us for an in-depth meetup co-hosted by O'Reilly Media and IBM featuring insightful talks, good food and drinks, and the opportunity for you to connect with the San Francisco AI community. The meetup will include talks by Nick Pentreath from IBM Watson, Romeo Kienzler from IBM Watson IoT, Lukas Biewald from CrowdFlower, and Amara Keller from IBM. You will walk away with the concrete technical know-how to implement your AI apps. We'll also discuss recent advances in the application of deep learning to recommender systems, and demonstrate the use of an anomaly detector, built using an LSTM Autoencoder Neural Network, a Raspberry Pi, NodeRED, the and IBM Data Science Experience. Amara Keller will demonstrate how to build a chatbot with Watson Conversation in under ten minutes and you will find out more about what developer events are happening locally.

More seriously, because deep learning is already creating tons of value--it's used in web search, advertising, speech recognition, recommender systems, and many more--it is clearly here to stay. Deep learning, plus more broadly other AI tools (graphical models, planning, KR, etc.), now has a clear path to transforming industry after industry. Its impact will be felt well beyond the tech world.

Wednesday was an exciting day for Canadian AI. Leaders in the Toronto tech community organized our energetic ecosystem to come together and discuss the real, buzzing potential -- and perhaps ethical responsibility -- of doing what it takes to claim our place as a global leader in AI. We were proud to sponsor the AI track and give our team the opportunity to share our values and some insights on our approach to applying AI to help companies maximize mutual lifetime value with customers. But we were most excited to hear from others, to build a few more bridges that, compounded across our community, might lead to 10 more Shopify platform companies we know we can create. To continue the momentum, we thought we'd share some of our key takeaways from the event.

At Qure, we regularly work on segmentation and object detection problems and we were therefore interested in reviewing the current state of the art. In this post, I review the literature on semantic segmentation. Although the results are not directly applicable to medical images, I review these papers because research on the natural images is much more mature than that of medical images. Post is organized as follows: I first explain the semantic segmentation problem, give an overview of the approaches and summarize a few interesting papers. In a later post, I'll explain why medical images are different from natural images and examine how the approaches from this review fare on a dataset representative of medical images.

If there are any doubts in regards to the popularity of Keras among the Data Scientist/Engineer community and the mindshare it commands, you just need to look at the support it has been receiving from all major AI and Cloud players. Currently the official Keras release already supports Google's TensorFlow and Microsoft's CNTK deep learning libraries besides supporting other popular libraries like Theano. Last year Amazon Web Services announced its support for Apache MXNet, another powerful Deep Learning library and few weeks ago support for Keras was added to the MXNet's next release candidate. As of now MXNet only seems to support Keras v1.2.2 and not the current Keras release 2.0.5. Although it is possible to deploy Keras models in production with any of the supported backends, developers and solution architects should keep in mind that Keras, by nature of being a high-level API for the different DL frameworks, doesn't yet support tweaking of all underlying parameters offered by the individual libraries.

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

Waymo, a driverless car company owned by Google parent company, Alphabet, has a fleet of driverless vehicles that have logged the company millions of kilometers driven on real city streets, but the bulk of its deep learning comes from driving on virtual roads. Developers for the company are training more than 25,000 autonomous vehicles over 12 million kilometers (8 million miles) of simulated roads driven every day. The simulations involve highly complex situations including multiple lane intersections and irregular vehicle traffic. Not only are these virtual driving schools informing deep learning models for each specific vehicle, they help to create data that allows driverless cars to network together. This adds an extra layer of safety for everyone including pedestrians and human-driven cars.