Two of the top numerical platforms in Python that provide the basis for Deep Learning research and development are Theano and TensorFlow. Both are very powerful libraries, but both can be difficult to use directly for creating deep learning models. In this post, you will discover the Keras Python library that provides a clean and convenient way to create a range of deep learning models on top of Theano or TensorFlow. Introduction to the Python Deep Learning Library Keras Photo by Dennis Jarvis, some rights reserved. Keras is a minimalist Python library for deep learning that can run on top of Theano or TensorFlow.

Amsterdam, Netherlands/Stuttgart, Germany – Artificial intelligence is poised to fundamentally change the world: in the future, machines will be capable of autonomously learning from experience and acting on this basis. The foundation for this is deep learning. In the future, the University of Amsterdam and Bosch will cooperate closely in this field. To this end, the two partners have announced a research alliance in Amsterdam. Known as Delta Lab ("Deep Learning Technologies Amsterdam"), the alliance aims to promote regular professional exchange and knowledge transfer.

Take for instance, the tired marketing mantra of getting the right message to the right customer at the right time. While that's long been a goal of marketers, Brandon Purcell, a senior analyst at Forrester, said at Forrester's AI Summit in New York yesterday that the the traditional marketing machine was only capable of identifying the "right customer" and "right time," not the "right message." There is something, however, that could make this into reality. Deep learning, according to Purcell, is a "fast evolving set of technologies and algorithms used by researchers, data scientists, and/or developers to build, train and test artificial neural networks that can be used as predictive models to probabilistically predict outcomes and/or identify complex patterns in data." Quite simply, deep learning is the marketing red pill. And with deep learning, Purcell says, brands and marketers should use these AI-marketing innovations to deliver the "right message."

The networks fight each other and improve together, like two humans playing against each other in a game.

You might think, based on the type of research they're doing, that the OpenAI office would be full of gadgets, full of wonder, full of weird experiments. There are no Faraday cages. Well, okay, there is a robot. And it's tucked away in a side room. It's surrounded by cobbled-together protective material so that it doesn't smash into itself if it starts flailing about due to a programming error.

Reinforcement learning is considered to be a strong AI paradigm which can be used to teach machines through interaction with the environment and learning from their mistakes. Despite its perceived utility, it has not yet been successfully applied in automotive applications. Motivated by the successful demonstrations of learning of Atari games and Go by Google DeepMind, we propose a framework for autonomous driving using deep reinforcement learning. This is of particular relevance as it is difficult to pose autonomous driving as a supervised learning problem due to strong interactions with the environment including other vehicles, pedestrians and roadworks. As it is a relatively new area of research for autonomous driving, we provide a short overview of deep reinforcement learning and then describe our proposed framework. It incorporates Recurrent Neural Networks for information integration, enabling the car to handle partially observable scenarios. It also integrates the recent work on attention models to focus on relevant information, thereby reducing the computational complexity for deployment on embedded hardware. The framework was tested in an open source 3D car racing simulator called TORCS. Our simulation results demonstrate learning of autonomous maneuvering in a scenario of complex road curvatures and simple interaction of other vehicles.

We describe and analyze a new boosting algorithm for deep learning called SelfieBoost. Unlike other boosting algorithms, like AdaBoost, which construct ensembles of classifiers, SelfieBoost boosts the accuracy of a single network. We prove a $\log(1/\epsilon)$ convergence rate for SelfieBoost under some "SGD success" assumption which seems to hold in practice.

When I tell people that I work at an AI company, they often follow up with "So what kind of machine learning/deep learning do you do?" This isn't surprising, as most of the market attention (and hype) in and around AI has been centered around Machine Learning, and its high profile subset, Deep Learning, and around Natural Language Processing, with the rise of the chatbot and virtual assistants. But while machine learning is a core component for artificial intelligence, AI is in fact more than just ML. So what does it really mean for an application to be "intelligent"? What does it take to create a system that is "artificially intelligent?

Google's DeepMind announced today that it was open sourcing Sonnet, its object-oriented neural network library. Sonnet doesn't replace TensorFlow, it's simply a higher-level library that meshes well with DeepMind's internal best-practices for research. Specifically, DeepMind says in its blog post that the library is optimized to make it easier to switch between different models when conducting experiments so that engineers don't have to upend their entire projects. To this avail, the team made changes to TensorFlow to make it easier to consider models as hierarchies. DeepMind also added transparency to variable sharing. It's in DeepMind's own interest to open source Sonnet.

Google has brought the late artist Bob Ross back to life, but as a monster-faced figure in a'nightmare' world. An engineer filtered an episode of Ross's PBS television show'The Joy of Painting' through the artificial neural network DeepDream, which can'see' objects and animals that are not really there. The video shows a segment with Ross painting his iconic happy trees, but instead of seeing fluffy green bushels, viewers are presented with bug-eyed creatures on the canvas. Google has brought the late artist Bob Ross back to life, but as a monster-faced figure in a'nightmare' world. An engineer filtered an episode of Ross's PBS television show'The Joy of Painting' through the artificial neural network DeepDream, which can'see' objects and animals that are not really there The latest DeepDream project was created by Alexander Reben, who is an artist an engineer.