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Nokia adopts blockchain to facilitate trusted sharing of data and AI models

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Nokia has announced the launch of its Data Marketplace, a blockchain-based service providing real-time access to massive trusted datasets. "Our customers need secure and trusted access to data for effective business decision making. With Nokia Data Marketplace, enterprises and CSPs can now benefit from richer insights and predictive models to drive digital ways of working and tap into new revenue streams." Nokia Data Marketplace accelerates AI initiatives through federated learning. This approach, combined with orchestration capabilities, facilitates collaborative development of highly accurate machine learning models for analytics use cases.


Understanding Palantir's Potential

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Palantir could kick off the adoption of AI and Blockchain across industry, by enabling organizations to create digital twins that these technologies can be deployed on, completely changing the way companies function. I have been eyeing Palantir recently. As with other tech companies, I find that the time that I spend working with different technologies has helped me understand what Palantir is all about very quickly. You see, we hear the tech buzzwords "AI" and "blockchain" a lot, but there are a lot of questions about how these technologies are going to drive the GDP needle. In the remainder of this post, I am going to breakdown how I believe Palantir could very well kick start the generalized adoption of AI and blockchain technologies across industry, resulting in better overall business performance for its customers and ultimately, in a solid business for itself.


Time Series Analysis of Cryptocurrencies Using Deep Learning & Fbprophet

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Artificial Intelligence is the root of both machine learning & deep learning, machine learning is a subset of artificial intelligence and deep learning is a subset of machine learning in that flow. Deep learning plays an important role in the advancement of artificial intelligence in many ways, using such an important feature for the prediction of data on daily basis gives better results and also helps in the understanding of various neglected sides. The cryptocurrency has been evolved and grown to a very large amount, estimating to a billion-dollar industry. Understanding such huge digital currency is difficult and also to estimate the change in trend is important, as a change in trend can lead to profit or loss of a particular cryptocurrency. The number of cryptocurrencies over the year has increased with new currency coming out, this introduction of digital currency can tell the demand of them in the market, due to the non-presence of such currency it becomes difficult to track the change, this is where deep learning would come in handy.


Global Machine Learning Operationalization Software Market Report Future Prospects, Growth …

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This latest Machine Learning Operationalization Software report published by Global Market Monitor covers the current market dynamics, and …


Deep Learning Market Trend and Future Forecast Till 2027 – Clark County Blog

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This has brought along several changes in This report also covers the impact of COVID-19 on the global market. The Deep Learning Market analysis summary by Reports Insights is a thorough study of the current trends leading to this vertical trend in various regions. In addition, this study emphasizes thorough competition analysis on market prospects, especially growth strategies that market experts claim. Deep Learning Market competition by top manufacturers as follow: Amazon Web Services (AWS), Google, IBM, Intel, Micron Technology, Microsoft, Nvidia, Qualcomm, Samsung Electronics, Sensory Inc., Skymind, Xilinx, AMD, General Vision, Graphcore, Mellanox Technologies, Huawei Technologies, Fujitsu, Baidu, Mythic, Adapteva, Inc., Koniku The global Deep Learning market has been segmented on the basis of technology, product type, application, distribution channel, end-user, and industry vertical, along with the geography, delivering valuable insights. To get this report at a profitable rate.: https://www.reportsinsights.com/discount/356220


Financial Engineering and Artificial Intelligence in Python

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Created by Lazy Programmer Team, Lazy Programmer Inc.Preview this Course - GET COUPON CODE Have you ever thought about what would happen if you combined the power of machine learning and artificial intelligence with financial engineering? Today, you can stop imagining, and start doing. This course will teach you the core fundamentals of financial engineering, with a machine learning twist. We will cover must-know topics in financial engineering, such as: Exploratory data analysis, significance testing, correlations, alpha and beta Time series analysis, simple moving average, exponentially-weighted moving average Holt-Winters exponential smoothing model Efficient Market Hypothesis Random Walk Hypothesis Time series forecasting ("stock price prediction") Modern portfolio theory Efficient frontier / Markowitz bullet Mean-variance optimization Maximizing the Sharpe ratio Convex optimization with Linear Programming and Quadratic Programming Capital Asset Pricing Model (CAPM) Algorithmic trading (VIP only) Statistical Factor Models (VIP only) Regime Detection with Hidden Markov Models (VIP only) In addition, we will look at various non-traditional techniques which stem purely from the field of machine learning and artificial intelligence, such as: Classification models Unsupervised learning Reinforcement learning and Q-learning ***VIP-only sections (get it while it lasts!) You will learn exactly why their methodology is fundamentally flawed and why their results are complete nonsense.


10 Best Artificial Intelligence Stocks to Buy for 2021

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In this article we will take a look at the 10 best artificial intelligence stocks for 2021. You can skip our detailed analysis of the AI industry's outlook for 2021 and some of the major growth catalysts for AI stocks and go directly to 5 Best Artificial Intelligence Stocks for 2021. Artificial intelligence is a buzzword increasingly being used by companies around the world that seek to project themselves at the forefront of cutting-edge research that promises to transform the lives of humans. As the word loses its meaning, it is important for investors to understand what artificial intelligence is and what companies stand to gain from breakthroughs in the new technology. Market estimates suggest that the artificial intelligence industry will witness a compound annual growth of more than 40% in the first half of this decade. Artificial intelligence, in the simplest words, uses data analytics to perform tasks that would otherwise be performed by humans.


Stock Forecast Based On a Predictive Algorithm

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This forecast is part of the Revolut Stock Trading Package, one of I Know First's algorithmic trading tools. The full investment universe includes the most promising stocks presented on Revolut trading platform. Package Name: Revolut Stock Trading Recommended Positions: Long Forecast Length: 3 Months (1/19/21 – 4/19/21) I Know First Average: 17.09% This Revolut Stock Trading Package forecast had correctly predicted 10 out of 10 stock movements. The highest trade return came from IVZ, at 32.4%.


Modular Machine Learning - Best Of Both Worlds?

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A Webinar By Joseph Simonian Abstract: After reviewing some differences between traditional statistics & data science, we present a modular machine learning framework for model validation which blends the two paradigms. Model validation is set up as a sequence of procedures, in which the output from one procedure serves as the input to another procedure within a single validation framework. An econometric model is used in the first module to classify data in an economically intuitive way. Proceeding modules apply data science techniques to evaluate the predictive characteristics of the model components. We apply the framework to the fundamental law of active management, a well-known formal characterization of portfolio managers alpha generation process.


Anomaly Detection in Time Series Data using Keras - Value ML

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In this project, we'll build a model for Anomaly Detection in Time Series data using Deep Learning in Keras with Python code.