Confirm your place early to avoid disappointment. All tickets are VAT included. Under VAT Excise regulations, attendees from all countries are required to pay VAT on all events taking place within the United Kingdom. For enquiries on ticket eligibility and paying via invoice please contact: hello@re-work.co Student/Academic passes are only available to those in full-time positions.

This blog is based on my talk in London at the Re.work Connected City Summit on Deep Learning Applications for Smart cities. The talk is based on a forthcoming paper created with the help of my students at UPM/citysciences on the same theme. Please email me at ajit.jaokar at futuretext.com or follow me @ajitjaokar for more details. Initially, we started off with the usual Smart City approach i.e. domains such as Security – Transport – Health – Governance – Environment etc Then, we were inspired by a statement "Man becomes the sex organs of the machine world – the bee of the plant world – enabling machines to evolve ever new forms" – Marshall McLuhan It indicates that disruptive innovations like Deep Learning and AI cannot be viewed in silos. What impact does it have on new services, culture, citizens?

Demis Hassabis, the cofounder and CEO of DeepMind, a Google-owned AI lab in London, is planning to buy a Tesla 3 from Elon Musk -- one of the company's earliest investors. Hassabis congratulated Musk on Twitter after Musk tweeted that 276,000 Model 3 orders had been made by the end of Saturday, just two days after the electric car was launched. "Really amazing to hear!! Just placing my order...," Hassabis wrote. At 35,000 ( 24,423), the five-seater Model 3 is the cheapest Tesla to date and is due to start shipping in late 2017. Those interested in owning a Tesla 3 need to put down 1,000 ( 702) deposits to reserve their vehicles.

Dr. Quoc Viet Le is a research scientist at Google Brain known for his path-breaking work on deep neural networks (DNN). He is especially famous for his Ph.D work in image processing under Andrew Ng, one of the pioneers of the DNN revolution. Le's and Ng's work demonstrated how computers could be used to learn complicated features and patterns in a way similar to how the mammalian brain learns. This revolutionized the interest in DNNs, and got the current giants of the computer industry such as Google, Facebook and Microsoft in a race to incorporate AI techniques into their software. DNNs perform effectively in tasks such as image processing, handwriting recognition and game-playing, and are being explored for solutions to other problems such as self-driving cars, robotics, medical diagnosis and environmental and social problems.

I am looking for an application for deep neural network development. Any suggestions (aside from the stock market) would be appreciated. Ideally, the application could use simulated data or reinforcement learning.

LIME - Local Interpretable Model-Agnostic Explanations -- Explains any machine learning models. Find Bug in 20 newsgroup classifier and understand the behavior of deep neural networks (homes.cs.washington.edu)

I wrote a blog post last month highlighting some of the exciting trends in the computing industry. One trend I discussed is the rapid progress in a branch of artificial intelligence called deep learning. You might have seen recent press coverage of a software developer named George Hotz who built his own self-driving car. I first met George a few months ago, and, like a lot of people who had seen the press coverage, I was skeptical. How could someone build such an advanced system all by himself?

TFlearn is a modular and transparent deep learning library built on top of Tensorflow. It was designed to provide a higher-level API to TensorFlow in order to facilitate and speed-up experimentations, while remaining fully transparent and compatible with it. The high-level API currently supports most of recent deep learning models, such as Convolutions, LSTM, BiRNN, BatchNorm, PReLU, Residual networks, Generative networks... In the future, TFLearn is also intended to stay up-to-date with latest deep learning techniques. Note: This is the first release of TFLearn.

TFlearn is a modular and transparent deep learning library built on top of Tensorflow. It was designed to provide a higher-level API to TensorFlow in order to facilitate and speed-up experimentations, while remaining fully transparent and compatible with it. The high-level API currently supports most of recent deep learning models, such as Convolutions, LSTM, BiRNN, BatchNorm, PReLU, Residual networks, Generative networks... In the future, TFLearn is also intended to stay up-to-date with latest deep learning techniques. Note: This is the first release of TFLearn.

In our view reinforcement learning is going to be as big as deep learning in the next two or three years.