Examples of using Machine learning model in English and their translations into Bengali
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Next, use that machine learning model to impute the survey answers of everyone in the digital trace data.
(2010) used the Galaxy Zoo classifications to train a machine learning model to do galaxy classification.
(2010) built a machine learning model that could predict the human classification of a galaxy based on the characteristics of the image.
Then, for a subset of the images,the Galaxy Zoo labels are used to train a machine learning model.
Finally, they used this machine learning model to estimate the sentiment of all 11 million posts.
Also, these projects can be done with open calls,whereby researchers compete to create machine learning models with the greatest predictive performance.
The concept of rationality of several machine learning models merging with their further transfer learning has been proposed and proved later.
Researchers interested in creating what I have called second generation human computation systems(e.g.,systems that use human labels to train a machine learning model) might be interested in Shamir et al.
First, for the people in both data sources, build a machine learning model that uses digital trace data to predict survey answers.
Using a machine learning model, these important cables were then compared with another dataset of important cables compiled decades later by professional historians.
(2010) used the Galaxy Zoo classifications to train a machine learning model to do galaxy classification.
If this machine learning model could reproduce the human classifications with high accuracy, then it could be used by Galaxy Zoo researchers to classify an essentially infinite number of galaxies.
First, for the people in both data sources, build a machine learning model that uses the big data source to predict survey answers.
Also, the machine learning models in these projects can be solicited with open calls, whereby researchers compete to create machine learning models with the greatest predictive performance.
New applications, powered by Hitachi's Lumada, incorporate cutting-edge machine learning models that can be rapidly customized for specific environments.
The common limiting factor of development and implementation ofsimilar systems was the lack of reliable technology that could provide a decentralised digital trustworthiness for final machine learning models and data sources.
Combining these two sources of data,they used the survey data to train a machine learning model to predict a person's wealth based on their call records.
Also, the machine learning models in these projects can be solicited with open calls, whereby researchers compete to create machine learning models with the greatest predictive performance.
Given this possibility,Blumenstock asked whether it was possible to train a machine learning model to predict how someone will respond to a survey based on their call records.
(2010) machine learning model were more complex than those in my toy example- for example, she used features like“de Vaucouleurs fit axial ratio”- and her model was not logistic regression, it was an artificial neural network.
At that point, researchers need to build second-generation systems wherehuman classifications are used to train a machine learning model that can then be applied to virtually unlimited amounts of data.
They used the survey data to train a machine learning model to predict someone's wealth from their call data, and then they used this model to estimate the wealth of all 1.5 million customers.
Researchers interested in creating what I have called computer-assisted human computation systems(e.g.,systems that use human labels to train a machine learning model) might be interested in Shamir et al.
Building a machine learning model that can correctly reproduce the human classifications is itself a hard problem, but fortunately there are already excellent books dedicated to this topic(Hastie, Tibshirani, and Friedman 2009; Murphy 2012; James et al. 2013).
More specifically, using the human classifications created by Galaxy Zoo,Banerji built a machine learning model that could predict the human classification of a galaxy based on the characteristics of the image.
Building a machine learning model that can correctly reproduce the human classifications is itself a hard problem, but fortunately there are already excellent books dedicated to this topic(Hastie, Tibshirani, and Friedman 2009; Murphy 2012; James et al. 2013).
At that point, researchers need to build a computer-assisted human computation system in whichhuman classifications are used to train a machine learning model that can then be applied to virtually unlimited amounts of data.
The features in Banerji and colleagues' machine learning model were more complex than those in my toy example- for example, she used features like“de Vaucouleurs fit axial ratio”- and her model was not logistic regression, it was an artificial neural network.
Further, foreshadowing a theme that will occur throughout this book, thesupervised learning approach that they used- hand-labeling some outcomes and then building a machine learning model to label the rest- turns out to be very common in social research in the digital age.
But, when compared to the machine learning focus of Google, Facebook's virtual reality bets in Oculus, or the augmented reality promise of Magic Leap, Apple looks stagnant and stuck in its exhausted revenue model of selling iPhones(there could be worse billion dollar places to be stuck in).