BigData News Thursday, March 1 Emergence phenomena, Predictive inference, Machine learning & more…

BigData News TLDR / Table of Contents

  • Unlike weak emergence, a strong emergent phenomenon cannot be simulated by a computer (at least by todays systems).
  • The larger the number of E-layer barriers, the less amount of information a feature can provide on the outcome due to the emergence phenomena.
  • Stock prediction: The stock market (or any other similar market) is a remarkable example of emergence.
  • Effect of emergence on example ML problems – – In this article we discussed possible effects of emergence phenomena on predictive inference and in turn on Machine Learning methods.
  • Emergence phenomena occur in complex systems where elementary parts interact with each other and give rise to totally new properties at a different level.

Tags: emergence phenomena, predictive inference, weak emergence, ,

  • Proficient in Machine Learning is a Must-Have on YourResumeTraining an algorithm to predict future outcomes, using a PCA algorithm to uncover clients personality traits, uploading a corpus of text to extract sentiment and grouping 650 000 lines of CRM and weblog data to cluster clients with machine learning all sounded…
  • In 5 years proficient in machine learning will be a must-have on any managers resume (just like project management or strategic thinking/analytical thinking is today).
  • This wouldnt have been possible 3 yearsago…Three years ago, computers werent better than humans at recognising images, algorithms were not so openly available, computational power was still on the edge, collecting data was not as easy and R and Python were completely necessary to start using A.I. for work.
  • Things like machine learning, neural networks, unsupervised learning, logistic regressions, deep learning, k-means algorithms, A.I., sentiment analysis, predictive analytics or image recognition shouldnt scare you.
  • To help you on your path, weve mapped them all out on this A.I. navigation cheat sheet specifically catered for growth and marketing: – GET THE A.I. Navigation Map HereI believe that others like me or the people we train who can grasp this new skill will be at a…

Tags: machine learning, unreachable rocket science, data scientist, correct timing, artificial intelligence

  • However,times have rapidly changed: DLT, commonlycalled blockchain, is revolutionising the traditionaland alternative asset managementsectors while producing real use cases inbanking, real estate, hedge funds, and privateequity (PE).
  • PE players face many challenges remediable,perhaps, by DLT.
  • For example, creating digital identifiers forportfolio companies would enable them togather and share information, which wouldstreamline investment analysis, decisionmaking,and due diligence processes.
  • Industry players must be on the lookout for newtools and start thinking about how DLT technologiesmay create value within their valuechains.
  • Under developmentin Luxembourg by KPMG, InTech andFundsquare, FundsDLT will interlink fund distributionsupply chain players, from custodiansto transfer agents to asset service providers,harmonising a currently fragmented ecosystem.

Tags: DLT, services. PE players, DLT technologies, york Paris PE, alternative asset management

  • In this series of exploratory blog posts, we explore the relationship between recurrent neural networks (RNNs) and IoT data.
  • In this article (Part One), we present the overall thought process behind the use of Recurrent neural networks and Time series applications – especially a type of RNN called Long Short Term Memory networks (LSTMs).
  • They solve the problem of feature engineering i.e. in finding out what is the best representation of the sample data to learn a solution to your problem – – When used for Time series forecasting, the obvious first problem is: How to model Time in the neural network?
  • For traditional applications of Neural networks (such as pattern recognition or classification), we do not need to model the Time dimension.
  • Recurrent neural networks are often used for modelling Time series.

Tags: time series, neural networks, recurrent neural networks, ,

  • Deep learning is a subfield of machine learning and it comprises several approaches to tackling the single most important goal of AI research: allowing computers to model our world well enough to exhibit something like what we humans call intelligence.
  • Google Translates science-fiction-like Word Lens function is powered by a deep learning algorithm and Deep Minds recent Go victory can also be attributed to DL although the triumphant algorithm AlphaGo isnt a pure neural net, but a hybrid, melding deep reinforcement learning with one of the foundational techniques of classical…
  • Deep learning is an ample approach to tackling computational problems that are too complicated to solve for simple algorithms, such as image classification or natural language processing.
  • It is quite possible that a large portion of the industries that currently leverage machine learning hold further unexploited potential for deep learning and DL-based approaches can trump current best practices in many of them.
  • We are pretty sure that deep learning is going to be the next big leapfrog ahead in the field of personalization as well.

Tags: deep learning, deep learning approaches, deep learning algorithm, basic conceptual level, triumphant algorithm AlphaGo

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