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Why Is Python Used for AI(Artificial Intelligence) & Machine Learning - eSparkBiz

#artificialintelligence

Artificial Intelligence and Machine Learning have been making our lives easier for quite some time. Today, we're going to talk about Python For AI & Machine Learning. Though the community keeps discussing the safety of its development, at the same time it is working relentlessly to grow the capacity and abilities of AI and ML. The demand for AI is at its peak, as it is highly used in analysing and processing large volumes of data. Due to the high volume and intensity of this work, it cannot be handled and supervised manually. AI is used in analytics for data-based predictions that enable people to come up with more effective strategies and strong solutions. FinTech applies AI in investment platforms to conduct market research and make predictions about where to invest funds for greater profits. The travel industry utilises AI to launch chatbots and make the user journey better. Python Web App Examples are proof of that. Due to such high processing power, AI and ML are absolutely capable of providing a better user experience, that is not only more apt but also more personal, making it more effective than ever.


Build No-code Automated Machine Learning Model with OptimalFlow Web App

#artificialintelligence

In the latest version(0.1.10) of OptimalFlow, it added a Flask-based'no-code' Web App as a GUI. Users could build Automated Machine Learning Models all by clicks, without any coding (Documentation). OptimalFlow was designed highly modularized at the beginning, which made it easy to continue developing. And users could build applications based on it. The web app of OptimalFlow is a user-friendly tool for people who don't have coding experience to build an Omni-ensemble Automated Machine Learning workflow simply and quickly.


New Data Processing Module Makes Deep Neural Networks Smarter

#artificialintelligence

Artificial intelligence researchers at North Carolina State University have improved the performance of deep neural networks by combining feature normalization and feature attention modules into a single module that they call attentive normalization (AN). The hybrid module improves the accuracy of the system significantly, while using negligible extra computational power. "Feature normalization is a crucial element of training deep neural networks, and feature attention is equally important for helping networks highlight which features learned from raw data are most important for accomplishing a given task," says Tianfu Wu, corresponding author of a paper on the work and an assistant professor of electrical and computer engineering at NC State. "But they have mostly been treated separately. We found that combining them made them more efficient and effective."


New data processing module makes deep neural networks smarter

#artificialintelligence

"Feature normalization is a crucial element of training deep neural networks, and feature attention is equally important for helping networks highlight which features learned from raw data are most important for accomplishing a given task," says Tianfu Wu, corresponding author of a paper on the work and an assistant professor of electrical and computer engineering at NC State. "But they have mostly been treated separately. We found that combining them made them more efficient and effective." To test their AN module, the researchers plugged it into four of the most widely used neural network architectures: ResNets, DenseNets, MobileNetsV2 and AOGNets. They then tested the networks against two industry standard benchmarks: the ImageNet-1000 classification benchmark and the MS-COCO 2017 object detection and instance segmentation benchmark.


Daily AI Roundup: The 5 Coolest Things On Earth Today

#artificialintelligence

AI Daily Roundup starts today! We are covering the top updates from around the world. The updates will feature state-of-the-art capabilities in artificial intelligence, Machine Learning, Robotic Process Automation, Fintech and human-system interactions. We will cover the role of AI Daily Roundup and their application in various industries and daily lives. SEMrush, leading online visibility management, and content marketing SaaS platform, has further expanded its ecosystem with the acquisition of a 100% stake in fast-growing public relations SaaS startup Prowly.com.


Optimizely updates its Full Stack platform with new data tools, enterprise integrations

ZDNet

San Francisco-based web experimentation company Optimizely is releasing a new version of its flagship platform that includes a new user interface, new data and analytics offerings, and integrations with AWS and Salesforce. Optimizely's core product is Web Experimentation, which enables non-technical staff to conduct A/B testing on the company's website using Optimizely's visual editor. Meantime, Optimizely's Full Stack product enables developers to experiment deeper into the tech stack to test things like search ranking algorithms or mobile app functionality. A/B testing has been a notable space of the software market as customers look to develop digital channels and improve experiences. Optimizely allows for quick A/B testing that can speed up software and code delivery.


How to run and share your deep learning web app on Colab?

#artificialintelligence

In my previous work, I built a deep learning model using Fastai to colorize black and white photos and built a web app prototype using Streamlit. I wish to share it with my colleagues and friends to get some feedback, so I tried to deploy the web app to AWS and Heroku. I was able to deploy my app to these cloud service providers and run it, however, the free tier accounts of them do not provide enough memory for my app, it crashed all the time. To have at least something running online, I had to make the size of the photos small, resulting in very poor quality. I do not want to spend a lot of money on upgrading the account at this early stage.


The Best Smart Water Leak Detectors

USATODAY - Tech Top Stories

The Flo by Moen Smart Water Leak Detector is a reliable gadget to help protect your home from water damage. Flo by Moen's Smart Water Leak Detector is the very best smart water leak detector we've ever tested, beating out our previous No. 1 pick, the Honeywell Home Water Leak Detector. While the two detectors are comparable, Moen is the more affordable of the two and, most of all, we were impressed by the near-instantaneous alerts (an imperative function of a smart leak detector) the Moen leak sensor sent to both of our iOS and Android devices, as well as via email. The Flo by Moen Smart Water Leak Detector passed all of our tests with flying colors and even continued to function after being submerged in water during our final round of testing. When a leak is detected, the sensor begins playing an alarm sound and flashes red, in addition to sending timely alerts in a matter of seconds. However, what it lacks in voice-control capabilities, it makes up for with a beautifully designed app full of useful data insights. The sensor also keeps track of the temperature and humidity within your home, which can help with moisture control.


My seven favorite Windows 10 features

ZDNet

A few weeks ago, I shared my list of Seven Windows 10 annoyances (and how to fix them). It was one of my most popular posts of the year, and so for a sequel I decided to offer the flip side, listing my seven favorite features of Windows 10. As I was putting this list together, one thing that struck me is how many of these features were either missing or poorly implemented when Windows 10 debuted in 2015. But all of the features I have chosen to spotlight here are well tested, well implemented, and guaranteed to make you more productive. Are you looking for Windows 10 Home or Windows 10 Pro?


The odyssey of Artificial Intelligence in business applications - Sentinelassam

#artificialintelligence

He can be contacted at m.bibhas@gmail.com) "Artificial intelligence is kind of the second coming of software". Instead of serving as a replacement for human intelligence and ingenuity, artificial intelligence is generally seen as a supporting tool. Prior to exploring the many ways how Artificial Intelligence (AI, hereafter) can be defined or recognise potential opportunities and challenges in machine or deep learning, common debates seem to first point out some of the ethical concerns that AI brings in the contemporary society. Policy makers and scientists thinks that AI: a) with increased automation technology would give rise to job losses, B) embodying the sophistication and complexity of AI would call for redeployment or retrain employees to keep them in jobs, C) will trigger the effect of continual machine interaction on human behaviour and attention; D) ignites the need to address algorithmic bias originating from human bias in the data; E) will develop the need to mitigate against unintended consequences, as smart machines are thought to learn and develop independently.