Deep Learning for the masses


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Back to the main feature …

An interesting blog in GigaOm by Derrick Harris on Deep Learning for the masses. What interested me most was Jeremy Howard from Kaggle.

DL-Dean-12

  • “…It’s going to enable whole new classes of products that have never existed before …”
  • But there’s a catch: deep learning is really hard. So far, only a handful of teams in hundreds of Kaggle competitions have used it. Most of them have included Geoffrey Hinton or have been associated with him.
    • Yep, it is hard. We are trying to bootstrap an application system and haven’t even scratched the surface – so it seems
  • If data scientists in places outside Google could simply (a relative term if ever there was one) input their multidimensional data and train models to learn it, that could make other approaches to predictive modeling all but obsolete.
    • Yep. Deel Learning is being applied in image recognition, translation et al. It would be interesting to see how the technologies can be applied to retail, banking, manufacturing et al

I also think the broader architecture of the three amigos viz Interface,Inference & Intelligence needs to come together

Finally,

Smarter Models = Smarter Apps – Yep, definitely !

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2 thoughts on “Deep Learning for the masses

  1. Pingback: Watson at Jeopardy – A Race Of Machines ? | My missives

  2. Pingback: Deep Learning – The Next Frontier ? | My missives

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