Tag: Deep Learning

Feed: AWS Government, Education, & Nonprofits Blog. Author: publicsector. Imagine visiting your doctor only to discover that you are losing your vision. That’s the reality for millions of people with diabetes every year around the world. Diabetic retinopathy causes progressive damage to the retina and is one of the leading causes of blindness. It afflicts more than 100 million people.  Early intervention helps in 90% of the cases, but often the diagnosis happens too late due to the cost, complexity, and expertise required for the scanning exam.Early last year, Stanford University students, Jason Su and Apaar Sadhwani, took the Project ... Read More

Feed: BDN Daily Press Releases Discussions - Big Data News. Author: Andrei Macsin. SEATTLE--Amazon Web Services, Inc. (AWS), an Amazon.com company (NASDAQ:AMZN), today announced the availability of P2 instances, a new GPU instance type for Amazon Elastic Compute Cloud (Amazon EC2) designed for compute-intensive applications that require massive parallel floating point performance, including artificial intelligence, computational fluid dynamics, computational finance, seismic analysis, molecular modeling, genomics, and rendering. With up to 16 NVIDIA Tesla K80 GPUs, P2 instances are the most powerful GPU instances available in the cloud. To get started with P2 instances, visit http://aws.amazon.com/ec2/instance-types/p2/.“The massive parallel floating point performance of ... Read More

Feed: Featured Blog Posts - Data Science Central. Author: William Vorhies. Summary:  What comes next after Deep Learning?  How do we get to Artificial General Intelligence?  Adversarial Machine Learning is an emerging space that points to that direction and shows that AGI is closer than we think. Deep Learning, Convolutional Neural Nets (CNNs) have given us dramatic improvements in image, speech, and text recognition over the last two years.  They suffer from the flaw however that they can be easily fooled by the introduction of even small amounts of noise, random or intentional.  This phenomenon can result in the CNN ... Read More

Feed: Featured Blog Posts - Data Science Central. Author: ajit jaokar. Introduction  According to Deloitte: by the “end of 2016 more than 80 of the world’s 100 largest enterprise software companies by revenues will have integrated cognitive technologies into their products”. Gartner also predicts that 40 percent of the new investment made by enterprises will be in predictive analytics by 2020. AI is moving fast into the Enterprise and AI developments can create value for the Enterprise. This value can be captured/visualized by considering an ‘Enterprise AI layer’. This AI layer is focussed on solving relatively mundane problems which are ... Read More

Feed: Planet big data. Author: David Smith. I just got back from Atlanta, the host of the Microsoft Machine Learning and Data Science Summit. This was the first year for this new conference, and it was a blast: the energy from the 1,000 attendees was palpable. I covered Joseph Sirosh's keynote presentation yesterday, but today I wanted to highlight a few other talks from the program now that the recordings are available to stream. On deep learning and machine intelligence: On data science techniques and processes: On production-scale data analysis with R: But for me personally, the highlight of the ... Read More

Feed: Planet big data. Author: Guest Blogger. by Anusua Trivedi, Microsoft Data Scientist This is part 3 of my series on Deep Learning, where I describe my experiences and go deep into the reasons behind my choices. In Part 1, I discussed the pros and cons of different symbolic frameworks, and my reasons for choosing Theano (with Lasagne) as my platform of choice. A very recent benchmarking paper compares CNTK with Caffe, Torch & TensorFlow, and CNTK performs substantially better than all the other three frameworks. In Part 2, I describe Deep Convolutional Neural Network (DCNN) and how Transfer learning ... Read More

Feed: Microsoft Azure Blog. Author: Jason Zander. Leading the Azure engineering organization over the past several years has been an incredible experience, and one that has taught me a great deal. My discussions with enterprise customers has given me an intimate understanding of the challenges IT teams (including our own) are facing today – How do I innovate with greater agility and faster time to market? How do I modernize our app portfolio? How do I maintain and optimize what I have? How do I manage and secure it all? Amidst all these challenges, however, lies a unique opportunity to ... Read More

Feed: Planet big data. Author: Sandeep Raut. Digital Transformation has become a burning question for all the businesses and the foundation to ride on the wave is being data driven. DJ Patil & Thomas Davenport mentioned in 2012 HBR article, that Data Scientist is the sexiest job of the century, and how true!  Even the latest Glassdoor ranked Data Scientist at 1st in top 25 best jobs in America. Over the last decade there’s been a massive explosion in both the data generated and retained by companies. Uber, Airbnb, Netflix, Wallmart, Amazon, LinkedIn, Twitter all process tons of data every ... Read More

Feed: Featured Blog Posts - Data Science Central. Author: Vincent Granville. Monday newsletter published by Data Science Central. Previous editions can be found here. The contribution flagged with a + is our selection for the picture of the week. Featured Resources and Technical Contributions A Relocation Guidebook for Data Scientists and More Real-Time Crime Forecasting Challenge Interesting Math Brain Teaser Concise Visual Summary of Deep Learning Architectures + Top 30 Free Web Scraping Software 10 Tools for Data Visualizing and Analysis for Business Generalized Dynamical Machine Learning - By PG Madhavan How to find out if it's correlation or causation ... Read More

Feed: Microsoft Azure Blog. Author: Fred Park. The Azure Big Compute team is happy to announce version 1.0.0 of the Batch Shipyard toolkit, which enables easy deployment of batch-style Dockerized workloads to Azure Batch compute pools. Azure Batch enables you to run parallel jobs in the cloud without having to manage the infrastructure. It’s ideal for parametric sweeps, Deep Learning training with NVIDIA GPUs, and simulations using MPI and InfiniBand. Whether you need to run your containerized jobs on a single machine or hundreds or even thousands of machines, Batch Shipyard blends features of Azure Batch -- handling complexities of ... Read More