The graph represents a network of 1,882 Twitter users whose tweets in the requested range contained "InsurTech", or who were replied to or mentioned in those tweets. The network was obtained from the NodeXL Graph Server on Friday, 01 April 2022 at 12:09 UTC. The requested start date was Friday, 01 April 2022 at 00:01 UTC and the maximum number of tweets (going backward in time) was 7,500. The tweets in the network were tweeted over the 2-day, 8-hour, 15-minute period from Tuesday, 29 March 2022 at 15:45 UTC to Friday, 01 April 2022 at 00:00 UTC. Additional tweets that were mentioned in this data set were also collected from prior time periods.
Learn how successful people trade and invest! Feel free to leave us your feedback. Become an expert in data analytics and real-world financial analysis. We are proud to present one of the most interesting and complete courses we've created so far. Through Mammoth Interactive's self-paced online learning, finance theory is not overwhelming like it would be in a regular university.
In this course, you'll learn about the emerging technologies in Artificial Intelligence and Machine Learning that are utilized in InsurTech and Real Estate Tech. Professor Chris Geczy of the Wharton School has designed this course to help you navigate the complex world of insurance and real estate tech, and understand how FinTech plays a role in the future of the industry. Through study and analysis of Artificial Intelligence and Machine Learning, you'll learn how InsurTech is redefining the insurance industry. You'll also explore classifications of insurtech companies and the size of the InsurTech, Real Estate Tech, and AI markets. You will also explore FinTech specialties with Warren Pennington from Vanguard.
Global Digital Skills Index research from Salesforce revealed a growing global digital skills crisis and the urgent need for action. The Index is based on over 23,000 workers in 19 countries reporting their readiness to acquire the key digital skills needed by businesses today and over the next five years. The Index follows a Salesforce-commissioned study by leading research institute RAND Europe to examine the evidence associated with various aspects of the digital skills gap. Reimagining business for the digital age is the number-one priority for many of today's top executives. We offer practical advice and examples of how to do it right.
Federated learning (FL) is a distributed machine learning (ML) technique that enables collaborative training in which devices perform learning using a local dataset while preserving their privacy. This technique ensures privacy, communication efficiency, and resource conservation. Despite these advantages, FL still suffers from several challenges related to reliability (i.e., unreliable participating devices in training), tractability (i.e., a large number of trained models), and anonymity. To address these issues, we propose a secure and trustworthy blockchain framework (SRB-FL) tailored to FL, which uses blockchain features to enable collaborative model training in a fully distributed and trustworthy manner. In particular, we design a secure FL based on the blockchain sharding that ensures data reliability, scalability, and trustworthiness. In addition, we introduce an incentive mechanism to improve the reliability of FL devices using subjective multi-weight logic. The results show that our proposed SRB-FL framework is efficient and scalable, making it a promising and suitable solution for federated learning.
The graph represents a network of 4,404 Twitter users whose tweets in the requested range contained "#marketing", or who were replied to or mentioned in those tweets. The network was obtained from the NodeXL Graph Server on Sunday, 16 January 2022 at 14:15 UTC. The requested start date was Sunday, 16 January 2022 at 01:01 UTC and the maximum number of days (going backward) was 14. The maximum number of tweets collected was 7,500. The tweets in the network were tweeted over the 2-day, 1-hour, 45-minute period from Thursday, 13 January 2022 at 18:00 UTC to Saturday, 15 January 2022 at 19:46 UTC.
Infocus International Group, a global business intelligence provider of strategic information and professional services, has launched a brand-new online training – FinTech & Artificial Intelligence will be commencing live on 11 May 2022. Banking is undergoing a transformation from being based in physical branches to using information technology (IT) and big data, together with highly specialized human capital. The value proposition of Fintech is to make complex processes easy, provide guidance and automation to fulfill heavy compliance burdens, and benefit from a great richness in data. Your organization has any means to commercialize the rewards of FinTech and artificial decision-making. Participants will learn how FinTech and AI can help to work more effectively and have a greater impact on business.
Coursera users in the UK have shown significant interest in classes on AI, cryptocurrency and other tech topics, according to data the company collected on the most popular online courses in 2021. Coursera based their data on responses from nearly 2.5 million UK-based users, finding a noticeable uptick in professional certificates centered around digital jobs. While the most popular course was from Yale University and focuses on mental health, the second most popular -- and 3rd most popular in 2020 -- was Stanford University's machine learning course. Google's Foundations: Data, Data Everywhere came in third place. Anthony Tattersall, a vice president at Coursera, said the survey's results suggests that UK learners are responding to the needs and opportunities that are defining the digital economy.
This tutorial will guide you through the simplest way to download historical cryptocurrency OHCL market data via exchange APIs. We'll use this data to train our Reinforcement Learning Bitcoin trading agent that could finally beat the market! Algorithmic trading is a popular way to address the rapidly changing and volatile environment of cryptocurrency markets. However, implementing an automated trading strategy is challenging and requires a lot of backtesting, which involves a lot of historical data and computational power. While developing a Bitcoin RL trading bot, I found out that it's pretty hard to get lower timeframe historical timeframe data.
This is the accepted version of an article with the same name, published in the Special Issue "Federated Learning and Blockchain Supported Smart Networking in Beyond 5G (B5G) Wireless Communication" in Computer Networks. Abstract Federated machine learning (FL) allows to collectively train models on sensitive data as only the clients' models and not their training data need to be shared. However, despite the attention that research on FL has drawn, the concept still lacks broad adoption in practice. One of the key reasons is the great challenge to implement FL systems that simultaneously achieve fairness, integrity, and privacy preservation for all participating clients. To contribute to solving this issue, our paper suggests a FL system that incorporates blockchain technology, local differential privacy, and zero-knowledge proofs. Our implementation of a proof-of-concept with multiple linear regression illustrates that these state-of-the-art technologies can be combined to a FL system that aligns economic incentives, trust, and confidentiality requirements in a scalable and transparent system. A Blockchain blockchain eliminates the need for a centralized authority, provides transparency, enforces the federated learning protocol, and provides a decentralized infrastructure for the collection of fees and the distribution of rewards. The reward payment is calculated based on the client's clients' Federated learning enables multiple clients FIM Research Center 1. Introduction The application of machine learning (ML) promises far-reaching potentials across industries . ML has already proven successful in many areas, such as web search or recommender systems in e-commerce, in which a lot of high-quality data exists . While researchers address ML's growing demand for compute power and use of data with, e.g., distributed ML approaches where multiple computing nodes share their resources [3, 4, 5] and quality issues with data processing, access to data is not only a technical issue. Both traditional ML and distributed ML approaches assume that their training data is centralized by nature, preventing the applicability of ML approaches to domains in which data is sensitive and distributed at the same time. To avoid that ML approaches must rely on data to which only a centralized organization or individual has full access, federated machine learning (FL) can aggregate the less sensitive ML models that were independently and locally trained by individual clients [6, 7].