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

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

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szilard

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

    Szilárd Pafka, PhD Chief Scientist, Epoch (USA) eRum Conference, Budapest May 2018
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    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
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    structured/tabular data: GBM (or RF) very small data: LR very

    large sparse data: LR with SGD (+L1/L2) images/videos, speech: DL
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    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
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    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
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    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
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    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
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    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
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    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
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    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
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    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
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    10x

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    10x

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