Lecture 1 introduces the concept of Natural Language Processing (NLP) and the problems NLP faces today. The concept of representing words as numeric vectors is then introduced, and popular approaches to designing word vectors are discussed. This lecture series provides a thorough introduction to the cutting-edge research in deep learning applied to NLP, an approach that has recently obtained very high performance across many different NLP tasks including question answering and machine translation. It emphasizes how to implement, train, debug, visualize, and design neural network models, covering the main technologies of word vectors, feed-forward models, recurrent neural networks, recursive neural networks, convolutional neural networks, and recent models involving a memory component. For additional learning opportunities please visit: http://stanfordonline.stanford.edu/

Developing training programmes for a large group of sales or technical or services personnel is a challenging task as the programme is meant for a diverse group, and has to be engaging and meaningful for the participants. The programmes are mostly delivered at multiple locations, they have to be updated from time to time and at times, also require to be culturally sensitive to remain relevant as well as contemporary. Effective assessment strategy is also important to ensure the programmes meet the stated business objectives. In the digital era, there is a plethora of content available on the internet. A lot of it is free of cost via options such as MOOCs, Course Era, You Tube and others.

This lecture series provides a thorough introduction to the cutting-edge research in deep learning applied to NLP, an approach that has recently obtained very high performance across many different NLP tasks including question answering and machine translation. This lecture series provides a thorough introduction to the cutting-edge research in deep learning appli... more

A quick, hassle-free way to stay on top of robotics news, our robotics digest is released on the first Monday of every month. Sign up to get it in your inbox. March is a month for change and new beginnings. The new UK Budget promised hundreds of millions of pounds to scientists and researchers to develop solutions to hi-tech challenges, including artificial intelligence and robotics, next generation batteries and new techniques for manufacturing medicines. The government is expected to allocate more than £500 million from the National Productivity Investment Fund so that UK companies might lead the way in the new technologies set to transform the world.

In this tutorial we will begin by laying out a problem and then proceed to show a simple solution to it using a Machine Learning technique called a Naive Bayes Classifier. This tutorial requires a little bit of programming and statistics experience, but no prior Machine Learning experience is required. You work as a software engineer at a company which provides email services to millions of people. Lately, spam has a been a major problem and has caused your customers to leave. Your current spam filter only filters out emails that have been previously marked as spam by your customers.

If you're a software engineer (or someone who's learning the craft), chances are that you've heard about deep learning (which we'll sometimes abbreviate as "DL"). It's an interesting and rapidly developing field of research that's now being used in industry to address a wide range of problems, from image classification and handwriting recognition, to machine translation and, infamously, beating the world champion Go player in four games out of five. A lot of people think you need a PhD or tons of experience to get a job in deep learning, but if you're already a decent engineer, you can pick up the requisite skills and techniques pretty quickly. Important point: You need motivation and the ability to code and problem solve well. Here at Deepgram we're using deep learning to tackle the problem of speech search.

The trend towards digital transformation of the enterprise, regardless of industry or sector, will accelerate in 2017 from the already significant levels seen last year. We identify the following seven digital technology trends as game changers for software-driven enterprises. The crumbling of the barriers to entry for machine intelligence – driven by the availability of high-quality open-source software components; cloud platforms from all major providers; and the availability of wildly popular and high-quality introductory courses on MOOC platforms – will drive growing mainstream adoption of machine intelligence as a differentiating and foundational technology layer in the digital transformation stack for identifying and closing new revenue opportunities, customizing user experience, driving operational efficiencies, and predicting failures. Also expect to see acceptance and greater adoption of advanced machine learning techniques for delivering closed-loop actionable insights in domains such as the Industrial Internet of Things and cybersecurity. The fully distributed, transparent, tamper-resistant, and auditable shared ledger technology known as blockchain is particularly powerful in settings where multiple parties need to reconcile without a central intermediary, or need to track provenance of assets across organizational boundaries, or need to establish and enforce contracts between untrusting parties and speed up reconciliation with a secure and verifiable audit trail.

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This webinar focuses on demonstrating how the Data Science Virtual Machine (DSVM) in Microsoft Azure conveniently enables key end-to-end data analytics scenarios by providing users immediate access to a collection of the top data science and development tools of the industry, completely pre-configured, with worked out examples and sample code. We will do a detailed demonstration of some key capabilities of the DSVM by working through a selection of popular scenarios using technologies that are enabled by it. These examples encompass areas such as using a local Spark environment for easy test and development, training and scoring for deep-learning on GPU based instances of the DSVM, cross-platform data exploration and querying using Apache Drill, and in-database analytics using SQL Server 2016 R Services. Both the Windows and Linux flavors of the VMs are covered in this webinar.

March 29, 2017 -- Interop ITX, the independent conference for tech leaders, today announced new artificial intelligence (AI) components created to further the education of its IT community. This year's event will explore both the education and practical application of AI through the all new Data & Analytics track, AI Theater and Demo Showcase, and The Future of Data Summit. AI content will explore a range of areas from business strategy and case studies, to specific tasks and application usage. Interop ITX will take place May 15 – 19, 2017 at the MGM Grand in Las Vegas, NV. For more information and to register, please visit: interop.com/.