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Applying Machine Learning To The E.Coli Class Imbalance Dataset

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The E.Coli dataset is a very popular dataset to experiment on because it is a multi-classification that has several imbalances. The E.coli dataset is such a difficult dataset to find a solution for that I have not been able to find a lot written about it on the internet. Jason Brownlee, of masteringmachinelearning.com suggested deleting the rows deleting the rows of the highly imbalance classes, but in my opinion such a practice defeats the purpose of endeavouring to make predictions. After much exhaustive research I was able to come up with a solution where all eight classes in the dataset were identified and predicted on. The E.coli dataset is credited to Kenta Nakai and was developed into its current form by Paul Horton and Kenta Nakai in their 1996 paper titled "A Probabilistic Classification System For Predicting The Cellular Localization Sites Of Proteins."


Adobe's new AI experiment syncs your dance moves perfectly to the beat

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TikTok has made on-beat dances and movements mainstream. But what if you went offbeat or the video you recorded had some lag to your perfect dance moves. Adobe can fix that for you. The company showed off an AI-powered experiment at its Adobe Max conference that syncs your off-beat movement to the beat of the music. Researchers used computer vision to follow the body movement of the person in the video.


Can We Trust AI Doctors? Google Health and Academics Battle It Out

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Machine learning is taking medical diagnosis by storm. From eye disease, breast and other cancers, to more amorphous neurological disorders, AI is routinely matching physician performance, if not beating them outright. Yet how much can we take those results at face value? When it comes to life and death decisions, when can we put our full trust in enigmatic algorithms--"black boxes" that even their creators cannot fully explain or understand? The problem gets more complex as medical AI crosses multiple disciplines and developers, including both academic and industry powerhouses such as Google, Amazon, or Apple, with disparate incentives.


Lost in Translation: How Artificial Intelligence is Breaking the Language Barrier - DefinedCrowd

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Human interaction with machines has experienced a great leap forward in recent years, largely driven by artificial intelligence (AI). From smart homes to self-driving cars, AI has become a seamless part of our daily lives. Voice interactions play a key role in many of these technological advances, most notably in language translation. Here, AI enables instant translation across a number of mediums: text, voice, images and even street signs. The technology works by recognizing individual words, then leveraging similarities in how various languages express the relationships between those words.


Best of arXiv.org for AI, Machine Learning, and Deep Learning – September 2020 - insideBIGDATA

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Researchers from all over the world contribute to this repository as a prelude to the peer review process for publication in traditional journals. The articles listed below represent a small fraction of all articles appearing on the preprint server. They are listed in no particular order with a link to each paper along with a brief overview. Links to GitHub repos are provided when available. Especially relevant articles are marked with a "thumbs up" icon.


A practical guide to RNN and LSTM in Keras

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After going through a lot of theoretical articles on recurrent layers, I just wanted to build my first LSTM model and train it on some texts! But the huge list of exposed parameters for the layer and the delicacies of layer structures were too complicated for me. This meant I had to spend a lot of time going through StackOverflow and API definitions to get a clearer picture. This article is an attempt to consolidate all of the notes which can accelerate the process of transition from theory to practice. The goal of this guide is to develop a practical understanding of using recurrent layers like RNN and LSTM rather than to provide theoretical understanding.


Robotic scrubbers growing at Sam's – IAM Network

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Walmart Inc.'s warehouse division Sam's Club is adding 372 floor-scrubbing robots to its fleet, putting the devices in all 599 U.S. clubs. Sam's Club is also expanding a pilot program to test shelf inventory technology that can be added to the robots. A Sam's Club spokeswoman said Thursday that the floor cleaners will give employees more time to focus on serving club members. Walmart stores use the same autonomous floor scrubbers that are powered by an operating system developed by Brain Corp. and made by Tennant Co. Walmart said in late 2018 it was adding 360 of the robots to an initial 100 used in a pilot program. In April 2019, the Bentonville-based retailer said it would have the floor cleaners in 1,860 stores by the following February.


Artificial intelligence and national security: Integrating online data – IAM Network

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How Retailers Use Artificial Intelligence to Know What You Want to Buy Before You Do – IAM Network

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MIAMI, FLORIDA – AUGUST 19: A sign is seen outside of a Target store on August 19, 2020 in Miami, Florida. The Terminator, a symbol of artificial intelligence run amok, famously declared that he would be back. Three and a half decades later, it turns out repeat business is at the heart of AI. In the years since, consumers have grown more comfortable sharing their data, especially as they crave more personalization. Now, with Covid-19 propelling shopping online--replete with tracking cookies and apps--we've reached a key moment.