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Better than Deep Learning: Gradient Boosting Machines (GBM) in R - eRum Conference - Budapest, May 2018

szilard
May 11, 2018
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Better than Deep Learning: Gradient Boosting Machines (GBM) in R - eRum Conference - Budapest, May 2018

szilard

May 11, 2018
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  1. Better than Deep Learning: Gradient Boosting Machines (GBM) in R

    Szilárd Pafka, PhD Chief Scientist, Epoch (USA) eRum Conference, Budapest May 2018
  2. Disclaimer: I am not representing my employer (Epoch) in this

    talk I cannot confirm nor deny if Epoch is using any of the methods, tools, results etc. mentioned in this talk
  3. ...

  4. structured/tabular data: GBM (or RF) very small data: LR very

    large sparse data: LR with SGD (+L1/L2) images/videos, speech: DL
  5. structured/tabular data: GBM (or RF) very small data: LR very

    large sparse data: LR with SGD (+L1/L2) images/videos, speech: DL it depends
  6. structured/tabular data: GBM (or RF) very small data: LR very

    large sparse data: LR with SGD (+L1/L2) images/videos, speech: DL it depends / try them all
  7. structured/tabular data: GBM (or RF) very small data: LR very

    large sparse data: LR with SGD (+L1/L2) images/videos, speech: DL it depends / try them all / hyperparam tuning
  8. structured/tabular data: GBM (or RF) very small data: LR very

    large sparse data: LR with SGD (+L1/L2) images/videos, speech: DL it depends / try them all / hyperparam tuning / ensembles
  9. structured/tabular data: GBM (or RF) very small data: LR very

    large sparse data: LR with SGD (+L1/L2) images/videos, speech: DL it depends / try them all / hyperparam tuning / ensembles feature engineering
  10. structured/tabular data: GBM (or RF) very small data: LR very

    large sparse data: LR with SGD (+L1/L2) images/videos, speech: DL it depends / try them all / hyperparam tuning / ensembles feature engineering / other goals e.g. interpretability
  11. structured/tabular data: GBM (or RF) very small data: LR very

    large sparse data: LR with SGD (+L1/L2) images/videos, speech: DL it depends / try them all / hyperparam tuning / ensembles feature engineering / other goals e.g. interpretability the title of this talk was misguided
  12. structured/tabular data: GBM (or RF) very small data: LR very

    large sparse data: LR with SGD (+L1/L2) images/videos, speech: DL it depends / try them all / hyperparam tuning / ensembles feature engineering / other goals e.g. interpretability the title of this talk was misguided but so is recently almost every use of the term AI
  13. 10x

  14. 10x