machine learning


PAIR-code/deeplearnjs

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We provide two APIs, an immediate execution model (think NumPy) and a deferred execution model mirroring the TensorFlow API. See the TypeScript starter project and the ES6 starter project to get you quickly started. To use a specific version, add @version to the unpkg URL above (e.g. After importing the library, the API will be available as dl in the global namespace. We recommend using Visual Studio Code for development.


Deep Learning for Disaster Recovery – Insight Data

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With global climate change, devastating hurricanes are occurring with higher frequency. After a hurricane, roads are often flooded or washed out, making them treacherous for motorists. According to The Weather Channel, almost two of every three U.S. flash flood deaths from 1995–2010, excluding fatalities from Hurricane Katrina, occurred in vehicles. During my Insight A.I. Fellowship, I designed a system that detects flooded roads and created an interactive map app. Using state of the art computer vision deep learning methods, the system automatically annotates flooded, washed out, or otherwise severely damaged roads from satellite imagery.


Improve WordPress Design & Dev through WireDelta's AI

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Disclaimer: The views expressed in this article are WireDelta's own and do not reflect the official policy of Cloudways. AI has become a mainstream business tool in many industries. From commercial banking to R&D, AI has simplified and streamlined processes on all levels. In fact, AI is also a key player in sectors where it is hard to imagine the use cases of AI based tools and platforms. Web development and design is one such area where AI is doing wonders.


Accenture Launches Interactive Learning Platform to Help Clients Transform Their Technology Talent

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We crafted a scalable, cost-effective approach for a new era of learning that puts the spotlight on --learning anytime, anywhere-- through digital technologies.-- With the Accenture Future Talent Platform, the client can now launch new services on its ecommerce site 75--percent faster than previously possible. The program will identify new roles and skills and build a training plan for a pilot, followed by a 40,000-person rollout. Accenture will also develop a curated, interactive curriculum for bank employees. Combining unmatched experience and specialized skills across more than 40 industries and all business functions -- underpinned by the world--s largest delivery network -- Accenture works at the intersection of business and technology to help clients improve their performance and create sustainable value for their stakeholders.


kieranbrowne/clojure-tensorflow

@machinelearnbot

TensorFlow requires at least Java 8. This will already be the default on most machines, but if it isn't for you, it's possible to force lein to use it by adding the:java-cmd "/path/to/java" key to your project.clj. Distributed under the Eclipse Public License either version 1.0 or (at your option) any later version.


We May Have Just Uncovered a Serious Problem With How AI "See"

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People with certain visual impairments aren't allowed to drive, for fairly obvious reasons. Now, a study from the University of Washington (UW) has shown that artificial intelligences (AI) aren't immune to vision problems when operating motor vehicles either. The researchers have determined that machine learning models can be prone to a kind of physical-world attack that impedes their ability to process images. Concretely, AI can have problems reading defaced street signs. For their study, the researchers focused on two potential types of physical attacks.


The impact of self-learning software now and in the foreseeable future

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We've spent so long wringing our hands and worrying about artificial and virtual intelligence that we forgot to roll out the welcome mat when they finally arrived. Now, when major tech companies give their annual keynotes, they can't help but pepper the narrative with phrases like "machine learning." What does it all mean, though? Should we crank up the worry now that it looks like every tent-pole feature of self-learning software could also be a critical flaw? The future is here -- and it's equal parts exciting and terrifying.


Google's AIY Vision Kit Augments Pi With Vision Processor

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Google has announced their soon to be available Vision Kit, their next easy to assemble Artificial Intelligence Yourself (AIY) product. You'll have to provide your own Raspberry Pi Zero W but that's okay since what makes this special is Google's VisionBonnet board that they do provide, basically a low power neural network accelerator board running TensorFlow. The VisionBonnet is built around the Intel Movidius Myriad 2 (aka MA2450) vision processing unit (VPU) chip. See the video below for an overview of this chip, but what it allows is the rapid processing of compute-intensive neural networks. We don't think you'd use it for training the neural nets, just for doing the inference, or in human terms, for making use of the trained neural nets.


Artificial intelligence and deep learning could usher in 24/7 advisors The Insurance and Investment Journal

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Artificial intelligence and deep learning may soon deliver 24/7 advisors that double as insurance fraud detectors. First, insurers must learn to use data. Stéphane Tremblay, team leader at the National Research Council's (NRC) Data Analytics Centre, specializes in automated learning. The Analytics Centre helps businesses in all sectors face the challenge of big data. The NRC invests $1 billion in research and development each year.


Getting started with a TensorFlow surgery classifier with TensorBoard data viz

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The most challenging part of deep learning is labeling, as you'll see in part one of this two-part series, Learn how to classify images with TensorFlow. Proper training is critical to effective future classification, and for training to work, we need lots of accurately labeled data. In part one, I skipped over this challenge by downloading 3,000 prelabeled images. I then showed you how to use this labeled data to train your classifier with TensorFlow. In this part we'll train with a new data set, and I'll introduce the TensorBoard suite of data visualization tools to make it easier to understand, debug, and optimize our TensorFlow code.