This project follows the description of the Deep Q Learning algorithm described in Playing Atari with Deep Reinforcement Learning [2] and shows that this learning algorithm can be further generalized to the notorious Flappy Bird. It is a convolutional neural network, trained with a variant of Q-learning, whose input is raw pixels and whose output is a value function estimating future rewards. Since deep Q-network is trained on the raw pixel values observed from the game screen at each time step, [3] finds that remove the background appeared in the original game can make it converge faster. The architecture of the network is shown in the figure below. The first layer convolves the input image with an 8x8x4x32 kernel at a stride size of 4. The output is then put through a 2x2 max pooling layer.

In this special guest feature, Gary Baum, Vice President of Marketing at MyScript, talks about how handwriting recognition is enhancing machine (and human) learning. As an input method, handwriting recognition teaches machines to adapt to the user, adding in another layer to their evolving skill set. Those users can program systems simply by jotting down notes and in turn, build platforms most reflective of the human experience. Gary is a tech industry veteran with more than 20 years of executive marketing and product management experience. At MyScript, he oversees global marketing activities and educational efforts to build MyScript brand awareness and drive technology adoption, expand and commercialize digital ink technology offerings, and nurture strategic collaborations and partnerships within the digital writing ecosystem.

If a neural network can write Shakespeare, could it write a resume for you? Inspired by the remarkable results of Recurrent Neural Networks and using thousands of anonymized resumes from untapt, I've been experimenting with applying deep learning techniques to the CV. There's a seminal blog post called The Unreasonable Effectiveness of Recurrent Neural Networks by Andrej Karpathy, a PhD student and instructor at Stanford. Andrej is taken by surprise by the magical results from Recurrent Neural Networks, a particular type of neural network that can process arbitrary sequences of inputs. In one example, he feeds the Complete Works of Shakespeare into an RNN.

The Go contest between Lee Sedol and AlphaGo, Google DeepMind's Go-playing computer program, was broadcast across South Korea. Scrambling to respond to the success of Google DeepMind's world-beating Go program AlphaGo, South Korea announced on 17 March that it would invest 863 million (1 trillion won) in artificial-intelligence (AI) research over the next five years. The commitment includes an already-budgeted 138.8 billion won for 2016; if the rest is spread evenly over the following four years, it represents a 55% increase in annual funding for AI. The windfall includes money for the founding of a high-profile, public–private research centre with participation from several Korean conglomerates, including Samsung, LG Electronics and Hyundai Motor, as well as the technology firm Naver, based near Seoul. "Thanks to the'AlphaGo shock', we have learned the importance of AI before it is too late" The timing of the announcement indicates the impact in South Korea of AlphaGo, which two days earlier wrapped up a 4–1 victory over grandmaster Lee Sedol in an exhibition match in Seoul.

This article is an effort to make you into a "semi-expert" in artificial intelligence, cognitive computing, deep learning and neural networks from scratch. Here I will share a few cool learning resources for these topics. These resources include documentaries, TED talks, online lecture videos, and books. There are several videos and online books included in this post to help you learn these concepts. These resources vary from introductory to advanced learning.

Taking after four losses, one of the world's top Go players – Lee Se-dol – has beaten Google DeepMind's AlphaGo program. Lee Se-dol, who has been defined as the Roger Federer of Go, has so far just figured out how to beat this AI once out of his four played games, so finally, AlphaGo has already won the set. Back in October, AlphaGo played against and defeat the three time European Go champion Fan Hui, winning every one of the five games. It also looked like Lee Se-dol would lose each of the five of his games too yet figured out how to turn AlphaGo's triumphant streak on its head by causing the A.I. to make a fault that it couldn't recover from. The AlphaGo AI project is different from "expert" systems which use hand-created rulesets.

Microsoft today announced that it is making it easier for developers to use its Computational Network Toolkit (CNTK) to build their own deep learning applications. The company first open sourced this toolkit in April 2015, but at the time, it was hosted on Microsoft's own CodePlex site and was only available under a restrictive academic license. Now, the team is moving the project to GitHub and to the MIT open source license. While Microsoft's old license made the project accessible to academics, it wasn't really geared toward production usage and tinkering outside of the academic environment. With this new license -- and by having the project on GitHub -- Microsoft hopes to attract other users as well.

The DeepFund Agent will make trading decisions directly from raw market data using Deep Learning, Deep Reinforcement Learning and Unsupervised Learning. The Agent was ordered to maximize the value of our bank account... The DeepFund Agent learns to trade from its experience and improves itself to a superhuman level.

Hi all, I am looking for a practical and simple way to give a phrase a score based on the similarity to a corpus. For instance, let's say I have a small chat log as a corpus. Given two phrases "i am doing fine" and "car breaks tomorrow", the first sentence should have a higher score (or probability) than the second. I am interested in something easy to implement (or already implemented) solution, I m not doing research or trying to beat the state-of-the-art so deep learning and other complex solutions are probably not what I want.

Note: I don't provide personal support for custom changes in the code. For people just starting, I recommend Treehouse for online-learning. This code allows to get state-of-the-art results and a significant improvement ( 1% in F1-score) with respect to the results presented in the paper. In order to reproduce the results, make sure Theano is installed and the repository is in your PYTHONPATH, e.g run the command export PYTHONPATH /path/where/is13/is: PYTHONPATH. Recurrent Neural Network Architectures for Spoken Language Understanding by Grégoire Mesnil is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.