When to use Machine Learning or Deep Learning? 7wData

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Understanding which AI technologies to use to advance a project can be challenging given the rapid growth and evolution of the science. This article outlines the differences between machine learning and Deep learning, and how to determine when to apply each one. In both machine learning and Deep learning, engineers use software tools, such as MATLAB, to enable computers to identify trends and characteristics in data by learning from an example data set. In the case of machine learning, training data is used to build a model that the computer can use to classify test data, and ultimately real-world data. Traditionally, an important step in this workflow is the development of features – additional metrics derived from the raw data – which help the model be more accurate.


How Machines Make Decisions with Less Data

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This enables the software to effectively train itself. As a result, machines are able to make viable decisions using significantly less consumer data than would usually be required.


Machine learning requires a fundamentally different deployment approach

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Welcome to the first O'Reilly Radar column. We plan to use this column to cover topics related to the themes that have our attention these days: AI/ML; Next Economy and Future of the Firm; Next Architecture; and tech-driven innovation and disruption. We'll also venture outside of O'Reilly's core focus on technology practitioners to include how technology fits into the modern economy, and offer the kind of information that can provide guidance and confidence to technology leaders facing this brave new world. The Radar team uses a combination of input from our wide-ranging social network, our own experience as practitioners, and data analysis (particularly from analyzing aggregate search and usage data on the O'Reilly online learning platform) to contextualize trends around technology adoption and to consider the impact of those trends. Put another way, we use our intuition and social network to vet our math, and we use math to vet our intuition and what our community tells us.


Top 5 #AI #MachineLearning and #Datascience Tweets for 21.10. 2019 Master Data Science 21.10.2019

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Contrary to the popular belief that #AI will wipe out jobs in the market, this #deeptech will bring enormous opportunities in every sector demanding rapid skill upgradation & significant shift in human-machine ecosystem. Your email address will not be published.


12 Tech Experts Reflect On The Most Important Technology Developments Of 2019

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In recent decades, our world has moved away from the analog and firmly into the digital realm. Technology is now incorporated into nearly every facet of our lives, both professional and personal. Modern technology will only continue to evolve in the years ahead, and consumers often look to industry insiders to identify the next technological game-changer. For a deeper dive, we asked the experts of Forbes Technology Council to weigh in on the most important tech developments of 2019. Here are the technologies they believe have been the most impactful this year and why.


Samuel Akinosho on LinkedIn: "#artificiaiintelligence #machinelearning "

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Amazing semi-weak supervision as an alternative approach from Facebook AI to train Artificial Intelligence systems such that we can do more with less labeled training data. It combines the merits of two different training methods: semi-supervised learning and weakly supervised learning. Very useful when large, high-quality labeled data sets are simply not available.


Machine Learning: Deploying Models at Scale - Dzone Research Guides

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Industry leaders discuss the latest trends in machine learning. We dive into using machine learning with microserivces, deploying machine learning models in real-life applications, and where the field is going over the next 12 months.


Software Engineer, Machine Learning in Tokyo - Mercari, Inc.

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Mercari is a marketplace app that makes it easy for people to safely sell and ship their things. Launched in 2013, the Mercari app has been downloaded over 100M times in Japan and the US. From fashion to toys, shoes to electronics and beyond, Mercari's mission is to create value in a global marketplace where anyone can buy and sell. Though we have over 1,800 employees, we still have a startup culture, where we encourage people to come up with big, crazy ideas, and to not be afraid of failure. Because the company is rapidly growing, you can set your own path, and there is enough transparency to allow our members to do so.


Rethinking Loss Prevention in Retail with Dell Technologies, NVIDIA, and Malong AI

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There is no shortage of ideas in which we might be able to use Machine Learning to change the way our businesses operate. This is certainly the case for many of the world's leading retailers who are looking at how to use Machine Learning to improve the overall operations of their business on many different fronts, from the edge to the core to the cloud. In this blog, we'll provide an overview of how we're able to provide a Machine Learning solutions to enhance the shopping experience in partnership with Malong and their RetailAI software stack. We are excited to share a tangible, working, use-case to show how Dell Technologies, in conjunction with NVIDIA's GPU-accelerated EGX platforms provide an unmatched experience in terms of inference throughput and latency. Let's start here, what is Machine Learning?


Q&A: Making AI accessible

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What are the practical applications of AI in healthcare? Dr Christoph Zindel, member of the Managing Board at Siemens Healthineers, responsible for the Imaging and Advanced Therapies business segments, has a clear vision. Healthcare IT News (HITN): Dr Zindel, you joined the Managing Board of Siemens Healthineers at the beginning of October. One of your stated goals is to champion digitisation and the use of AI in healthcare. From buzzword to applied technology?