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How to Measure the Success of a Recommendation System?

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Recommender systems are used in a variety of domains, from e-commerce to social media to offer personalized recommendations to customers. The benefit of recommendations for customers, such as reduced information overload, has been a hot topic of research. However, it's unclear how and to what extent recommender systems produce commercial value. It's challenging to create a reliable product suggestion system. However, defining what it means to be reliable is also a challenging task.


Top 10 Amazing Python Developers to Follow in 2021

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Python is one of the most widely used programming languages in the world, and for good reason. Because of its vast libraries and flexible structure, it's simple to learn, has consistent and easy-to-parse syntax, and is utilized for artificial intelligence applications. The platform's spectacular ascent has sparked a devoted community, fueled in no little part by its adoption by big companies such as DropBox, Reddit, and Instagram, to name a few. Check out this list of Python developers to follow if you're seeking Python programmers who are leading the charge. The people on this list have solid technical credentials, are constantly adding new and interesting features to the platform, and have a strong social media presence.


Artificial Intelligence vs Machine Learning vs Deep Learning- What's the difference in 2021

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Although Artificial Intelligence, Machine Learning and Deep Learning are often used interchangeably, are they the same thing? Data science has become the new sensation in today's world. With copious amounts of data being generated daily, it only makes sense for companies to make use of the technologies to make appropriate analyses to make sound decisions. Whether it's a recommendation on Netflix, or Google Maps, or a Ride on Uber, there are a lot of benefits and convenience provided by these technologies. Companies can leverage these technologies to provide a better experience, maximize sales and profits if they can leverage the data and predict the consumer behaviour and purchasing pattern-The right way.


Emerging Technology Trends Of 2021 - ONPASSIVE

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The rapid evolution of Technology is expanding and helps enable faster change and advancement in various fields, accelerating the rate of change to the point where it will eventually become exponential. However, it is not just technology trends and leading technologies that are changing; much more has changed this year due to the outbreak of COVID-19, which has caused IT workers to recognize that their jobs will not be the same in the contactless world. The Global pandemic has pushed digitization and automation, helping businesses thrive even in adversity. As a result, many companies are increasingly adopting disruptive technologies and changing their business strategies to sustain themselves. The impact of the pandemic will last a long time, and the digital shift will continue.


Pinaki Laskar on LinkedIn: #5G #artificialintelligence #IoT

#artificialintelligence

AI Researcher, Cognitive Technologist Inventor - AI Thinking, Think Chain Innovator - AIOT, XAI, Autonomous Cars, IIOT Founder Fisheyebox Spatial Computing Savant, Transformative Leader, Industry X.0 Practitioner The number of Internet of Things (IoT) devices worldwide is forecast to almost triple from 8.74 billion in 2020 to more than 25.4 billion IoT devices in 2030. In order to stabilize climate change, we need to hold Earth's temperature at 1.5 C above pre-industrial levels. This means we need to halve global greenhouse gas emissions by 2030 and reach net zero before 2050. On the frontline of digitalization lies 5G, itself an exponential technology, a platform enabling technologies such as #artificialintelligence (AI), blockchain, the internet of things (#IoT), quantum computing and extended reality (XR). The solutions are not hypothetical, they just need to be scaled up.


How Moveworks' AI platform broke through the multilingual NLP barrier

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Chatbots have a checkered past of often not delivering the performance their providers have promised. This is especially true in the IT service management (ITSM) and multilingual NLP spaces, where service desks found support teams deluged with complaints -- yes, about the support chatbots. Just getting English language nuance right and how enterprises communicate often require chatbots to be custom programmed with constraint and logic workflows supported with natural language processing (NLP) and machine learning. If that sounds like a science project, it is, and IT users are the test subjects. Because of their complexity, chatbots were contributing to already overflowing trouble-ticket queues.


Top 10 Amazing Python Developers to Follow in 2021

#artificialintelligence

Python is one of the most widely used programming languages in the world, and for good reason. Because of its vast libraries and flexible structure, it's simple to learn, has consistent and easy-to-parse syntax, and is utilized for artificial intelligence applications. The platform's spectacular ascent has sparked a devoted community, fueled in no little part by its adoption by big companies such as DropBox, Reddit, and Instagram, to name a few. Check out this list of Python developers to follow if you're seeking Python programmers who are leading the charge. The people on this list have solid technical credentials, are constantly adding new and interesting features to the platform, and have a strong social media presence.


Azure ML (AML) Alternatives for MLOps - neptune.ai

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Azure Machine Learning (AML) is a cloud-based machine learning service for data scientists and ML engineers. You can use AML to manage the machine learning lifecycle--train, develop, and test models, but also run MLOps processes with speed, efficiency, and quality. For organizations that want to scale ML operations and unlock the potential of AI, tools like AML are important. Creating machine learning solutions that drive business growth becomes much easier. But what if you don't need a comprehensive MLOps solution like AML? Maybe you want to build your own stack, and need specific tools for tasks like tracking, deployment, or for managing other key parts of MLOps? Experiment tracking documents every piece of information that you care about during your ML experiments. Machine learning is an iterative process, so this is really important. Azure ML provides experimental tracking for all metrics in the machine learning environment.


Multi-Cloud For Modern Enterprises - Why And Why Not? - Storage, Networking, Virtualization, Cloud and AI/ML

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Cloud adoption is accelerating fast in enterprises surging towards modernity. But are there better ways of utilizing the full potential of cloud computing? Leaving behind the constraints of a single cloud computing platform, you will find various other arrangements like hybrid and multi-cloud computing. The annual RightScale State of the Cloud Report suggests, 90% of respondents believe that multi-cloud is already the most common pattern with businesses and enterprises. So, let's delve into understanding more about multi-cloud for modern enterprises.


Google is AI first: Top 15 AI projects powering Google products

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We already covered how AI is integral to Alphabet. We had left out Google. As AI is starting to power all Google products, Google deserves its own focus. We are now witnessing a new shift in computing: the move from a mobile-first to an AI-first world. From smartphone assistants to image recognition and translation, a myriad of AI functionality hide within google apps that you daily use.