This screencasts describes how to import a Neural Network that was created and trained using Keras, into DeepLearning4J Deeplearning4j - Skymind uploaded a video 2 weeks ago Skymind Academy - Duration: 91 seconds. Skymind Academy enables your team to build deep learning solutions. We offer private corporate seminars and public workshops. Deeplearning4j - Skymind uploaded a video 3 weeks ago What is Deep Learning? We explain what deep learning is and why it matters.

All eyes are on Montreal these days as a hub for deep learning. "Clearly it's a place where everybody wants to be if we want to tap into that talent," says Nagraj Kashyap, corporate vice-president of Microsoft Ventures in San Francisco. Montreal's pre-eminence as a deep learning centre can largely be attributed to the efforts of Yoshua Bengio, considered to be one of the three "co-fathers" of deep learning technology. Bengio not only engaged in cutting-edge research at the Université de Montréal long before deep learning was considered viable; his work has spawned an ecosystem that many say is unrivalled in the artificial intelligence (AI) world. That ecosystem includes the Montreal Institute for Learning Algorithms (MILA) which has been funded by government and private sector parties, including Google and Microsoft, among other tech notables.

Network embeddings have become very popular in learning effective feature representations of networks. Motivated by the recent successes of embeddings in natural language processing, researchers have tried to find network embeddings in order to exploit machine learning algorithms for mining tasks like node classification and edge prediction. However, most of the work focuses on finding distributed representations of nodes, which are inherently ill-suited to tasks such as community detection which are intuitively dependent on subgraphs. Here, we propose sub2vec, an unsupervised scalable algorithm to learn feature representations of arbitrary subgraphs. We provide means to characterize similarties between subgraphs and provide theoretical analysis of sub2vec and demonstrate that it preserves the so-called local proximity. We also highlight the usability of sub2vec by leveraging it for network mining tasks, like community detection. We show that sub2vec gets significant gains over state-of-the-art methods and node-embedding methods. In particular, sub2vec offers an approach to generate a richer vocabulary of features of subgraphs to support representation and reasoning.

Given recent successes in AI (e.g., AlphaGo's victory against Lee Sedol in the game of GO), it's become increasingly important to assess: how close are AI systems to human-level intelligence? This paper describes the Allen AI Science Challenge---an approach towards that goal which led to a unique Kaggle Competition, its results, the lessons learned, and our next steps.

Jonathan Lazar (jlazar@towson.edu) is a professor of computer and information sciences and director of the Undergraduate Program in Information Systems at Towson University, Towson, MD, and recipient of the SIGCHI 2016 Social Impact Award. Elizabeth Churchill (churchill@acm.org) is a director of user experience at Google, San Francisco, CA, and Secretary/Treasurer of ACM. Tovi Grossman (tovi.grossman@autodesk.com) is a distinguished research scientist in the User Interface Research Group at Autodesk Research, Toronto, Canada. Gerrit C. van der Veer (gerrit@acm.org) is an emeritus professor of multimedia and culture at the Vrije Universiteit Amsterdam, the Netherlands, guest professor of human-media interaction at Twente University, Twente, the Netherlands, of human-computer and society at the Dutch Open University, Heerlen, Netherlands, of interaction design at the Dalian Maritime University, Dalian, China, and of animation and multimedia at the Lushun Academy of Fine Arts, Shenyang, China. Philippe Palanque (palanque@irit.fr) is a professor of computer science at Université Paul Sabatier Paul Sabatier – Toulouse III, France, and head of the Interactive Critical Systems research group of the IRIT laboratory, Toulouse, France. John "Scooter" Morris (scooter@cgl.ucsf.edu) is an adjunct professor in the Department of Pharmaceutical Chemistry at the University of California San Francisco and executive director of the Resource for Biocomputing, Visualization and Informatics, a U.S. National Institutes of Health Biomedical Technology Research Resource at the University of California San Francisco. Jennifer Mankoff (mankoff@cs.cmu.edu) is a professor in the Human Computer Interaction Institute at Carnegie Mellon University, Pittsburgh, PA.

In the last few months, I have had several people contact me about their enthusiasm for venturing into the world of data science and using Machine Learning (ML) techniques to probe statistical regularities and build impeccable data-driven products. However, I've observed that some actually lack the necessary mathematical intuition and framework to get useful results. This is the main reason I decided to write this blog post. Recently, there has been an upsurge in the availability of many easy-to-use machine and deep learning packages such as scikit-learn, Weka, Tensorflow etc. Machine Learning theory is a field that intersects statistical, probabilistic, computer science and algorithmic aspects arising from learning iteratively from data and finding hidden insights which can be used to build intelligent applications. Despite the immense possibilities of Machine and Deep Learning, a thorough mathematical understanding of many of these techniques is necessary for a good grasp of the inner workings of the algorithms and getting good results. There are many reasons why the mathematics of Machine Learning is important and I'll highlight some of them below: The main question when trying to understand an interdisciplinary field such as Machine Learning is the amount of maths necessary and the level of maths needed to understand these techniques.

The use of artificial intelligence and the "next-generation" of virtual learning environments (VLEs) are two areas of technology that have been forecast to have a major impact on higher education in the future, according to the expert panel of a major new report. The NMC Horizon Report: 2017 Higher Education Edition is produced by the New Media Consortium – a community of hundreds of universities, colleges, museums and research organisations driving innovation across their campuses – and is the flagship publication of the NMC Horizon Project, which analyses emerging technology uptake in education. Artificial intelligence, the report notes, has the "potential to enhance online learning, adaptive learning software, and research processes in ways that more intuitively respond to and engage with students". Samantha Adams Becker, senior director of publications and communications at NMC and the report's editor, said that the higher education world was already seeing the initial benefits of AI, which was "very much driving" the adaptive learning field. "If you think about online courses where there may be hundreds of students, it's currently very difficult for a professor or instructor to maybe get a good grasp on how students not only are performing, but are feeling about the material…as they're lecturing or a video's playing," she said. "Virtual avatars and chatbots…have the ability to assess that on an individual level, and if the student seems stuck then maybe you can replay part of the video.

We consider the minimization of composite objective functions composed of the expectation of quadratic functions and an arbitrary convex function. We study the stochastic dual averaging algorithm with a constant step-size, showing that it leads to a convergence rate of O(1/n) without strong convexity assumptions. This thus extends earlier results on least-squares regression with the Euclidean geometry to (a) all convex regularizers and constraints, and (b) all geome-tries represented by a Bregman divergence. This is achieved by a new proof technique that relates stochastic and deterministic recursions.

In this study, the authors develop a structural model that combines a macro diffusion model with a micro choice model to control for the effect of social influence on the mobile app choices of customers over app stores. Social influence refers to the density of adopters within the proximity of other customers. Using a large data set from an African app store and Bayesian estimation methods, the authors quantify the effect of social influence and investigate the impact of ignoring this process in estimating customer choices. The findings show that customer choices in the app store are explained better by offline than online density of adopters and that ignoring social influence in estimations results in biased estimates. Furthermore, the findings show that the mobile app adoption process is similar to adoption of music CDs, among all other classic economy goods. A counterfactual analysis shows that the app store can increase its revenue by 13.6% through a viral marketing policy (e.g., a sharing with friends and family button).

If you've been checking out the XDA Depot then you know about all the sweet deals you can get on software, online courses, and more. Some of the best values are the course bundles that have huge discounts. Let's take a look at The Complete Machine Learning Bundle which is currently 95% off. Master artificial intelligence and be ahead of the curve with 10 courses & 63.5 hours of training in machine learning. This course will get you prepared to solve problems with NLP, recommendations, sentiment analysis, quant trading and computer vision.