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Probability Distributions To Be Aware Of For Data Science (With Code)

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Probability and statistics knowledge is at the core of data science and machine learning; You'll require both statistics and probability knowledge to effectively gather, review, analyze and communicate with data. This means it's essential for you to have a good grasp of some fundamental terminologies, what they mean, and how to identify them. One such term you'll hear thrown around a lot is'distribution.' All this is in reference to is the properties of the data. There's several instances of phenomena in the real world that are considered to be statistical in nature (i.e. This means there are several instances in which we've been able to develop methodologies that help us model nature through mathematical functions that can describe the characteristics of the data.


The HR Guide to Machine Learning and AI

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Human Resources leaders are coming into their own among C-suite executives as they transform workplaces during these historic times. One of the important tasks at hand is better understanding technology, especially machine learning (ML) and artificial intelligence (AI), and the way it connects with data analytics to influence decision-making. HR professionals who gain an understanding of how they can use ML and AI to help them address all the big issues, including recruitment, employee engagement, career growth of employees, DEI, and more will have a competitive edge.


Best Egg - Senior Machine Learning Operations Engineer

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At Best Egg we believe everybody should have the opportunity to enjoy life. As a fast growing fintech, we are on a mission to help our customers gain easy access to funds and reach their financial goals. Our Best Egg personal loan product has an impeccable record and stands out in the industry with its strong value proposition and overwhelmingly positive customer reviews. Data is at the heart of everything we do. Join a Data Science team working at the cutting edge within our industry and constantly advancing our ML practice. Our Data Science team is dedicated to creating machine-learning models that power innovation and creative insights that pioneer new products and help our business reach the next level. We work on diverse projects across all business units within the company and have a direct engagement model between data scientists and business stakeholders. Join a collaborative group of Data Scientists skilled in predictive analytics and help them to deploy and monitor real-


Accelerating The Pace Of Machine Learning - AI Summary

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But some of them make their mark: testing, hardening, and ultimately reshaping the landscape according to inherent patterns and fluctuations that emerge over time. In the paper "Distributed Learning With Sparsified Gradient Differences," published in a special ML-focused issue of the IEEE Journal of Selected Topics in Signal Processing, Blum and collaborators propose the use of "Gradient Descent method with Sparsification and Error Correction," or GD-SEC, to improve the communications efficiency of machine learning conducted in a "worker-server" wireless architecture. "Various distributed optimization algorithms have been developed to solve this problem," he continues,"and one primary method is to employ classical GD in a worker-server architecture. "Current methods create a situation where each worker has expensive computational cost; GD-SEC is relatively cheap where only one GD step is needed at each round," says Blum. Professor Blum's collaborators on this project include his former student Yicheng Chen '19G '21PhD, now a software engineer with LinkedIn; Martin Takác, an associate professor at the Mohamed bin Zayed University of Artificial Intelligence; and Brian M. Sadler, a Life Fellow of the IEEE, U.S. Army Senior Scientist for Intelligent Systems, and Fellow of the Army Research Laboratory. But some of them make their mark: testing, hardening, and ultimately reshaping the landscape according to inherent patterns and fluctuations that emerge over time. In the paper "Distributed Learning With Sparsified Gradient Differences," published in a special ML-focused issue of the IEEE Journal of Selected Topics in Signal Processing, Blum and collaborators propose the use of "Gradient Descent method with Sparsification and Error Correction," or GD-SEC, to improve the communications efficiency of machine learning conducted in a "worker-server" wireless architecture. "Various distributed optimization algorithms have been developed to solve this problem," he continues,"and one primary method is to employ classical GD in a worker-server architecture.


Build an Image Duplicate Finder System: A Guide

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To start with, I need to define an important term. A query image is an image the user enters to obtain information. With the help of a similarity block, the system searches for similar images among a dataset, which computes how close the images are to each other. Image 1 illustrates the steps. In section 3, we will be looking into this similarity block and exploring the most common methods of achieving this functionality.


AI being used to grow tomatoes

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Five teams from the Netherlands, South Korea and China have advanced to the final stage of a competition to see who can grow fresh tomatoes in greenhouses remotely using artificial intelligence. The second Autonomous Greenhouse Challenge, which is organised by Dutch academic powerhouse Wageningen University & Research (WUR) and Chinese multinational conglomerate Tencent, began in September with a 24-hour hackathon involving 21 international teams and more than 200 participants from 26 countries. The five winning teams – Netherlands-based AiCU, The Automators and Automatoes, China'sIUA.CAAS and Korea'sDigilog – will each be given six months' access to a real greenhouse in the Dutch town of Bleiswijk, where from December onwards they will attempt to control and produce a tomato crop from afar by employing AI algorithms to keep inputs like water, nutrients and energy at sustainable levels. September's hackathon, held at WUR, saw an international jury award points to each team based on their composition and competence, their application of AI technology and the net profit they made during a virtual tomato production game. During their pitches, the teamswere given access to a climate model and a tomato crop growth model previously developed by researchers at WUR.


A Brief Overview of Machine Learning

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As we randomly search terms on the internet, we often encounter "machine learning" and "deep learning" and how they are revolutionizing the way in which we live our lives. At present, machine learning is almost used everywhere from self-driving cars, email spam detection, recommender systems that we see in Netflix and Amazon, credit card fraud detection used by banks and so on. The list goes on and on with potential new applications being created. Therefore, it is very important to stay updated with the latest trends and understand what machine learning actually is and get a good broader understanding of some of the types of machine learning. In this article, I would explain machine learning and the different categories of machine learning.


A Comprehensive Guide to Swin Transformer

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Swin Transformer (Liu et al., 2021) is a transformer-based deep learning model with state-of-the-art performance in vision tasks. Unlike the Vision Transformer (ViT) (Dosovitskiy et al., 2020) which precedes it, Swin Transformer is highly efficient and has greater accuracy. Due to these desirable properties, Swin Transformers are used as the backbone in many vision-based model architectures today. Despite its wide adoption, I find that there is a lack of articles with detailed explanation in this topic. Therefore, this article aims to provide a comprehensive guide to Swin Transformers using illustrations and animations to help you better understand the concepts.


Pantheon Lab Limited

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Our synthetic media A.I. technologies, with an extensive research on generative adversarial network (GAN), computer vision, computer graphics, voice and speech synthesis, enable the generation of audiovisual contents and countless application scenarios. We have demystified the process of creating high-fidelity virtual avatars down to a few clicks. We specialize in using deep learning to generate and manipulate visual content at scale. We can synthesize speech, with a natural and personalized voice, from text or another voice recordings.


Top Machine Learning Trends for 2022

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Blockchain is the new talk of the town. It is the technology behind cryptocurrencies like Bitcoin. Today, it has turned out to be a game-changer for businesses. Its decentralized ledger offers transparency and immutability in transactions between parties without any intermediary. The transactions are irreversible, which means once a ledger is updated, it can never be changed or deleted. Blockchain technology will eventually find its space in the new and innovative applications of Machine Learning and Artificial Intelligence.