Why Deep Learning Is Still Too Difficult

Written by alexdeterminedai | Published 2020/09/07
Tech Story Tags: deep-learning | data-science | artificial-intelligence | machine-learning | open-source | software-development | hyperparameter-optimization | convolutional-neural-network

TLDR The core concepts underlying even the latest deep learning models can be traced back to the early ‘70s when the first artificial neural networks were born. In the last few years, there has been an exponential growth of ML-related papers on Arxiv (nearly 100 new papers/day!) [1] Building practical applications powered by deep learning remains to be too expensive and too difficult for many organizations. We will also explain how those challenges differ from those of traditional machine learning systems.via the TL;DR App
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Written by alexdeterminedai | deep learning, not devops.
Published by HackerNoon on 2020/09/07
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