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Machine learning: How it can help your business...
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szilard
March 21, 2018
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Machine learning: How it can help your business - Microsoft Future Decoded - Budapest, March 2018
szilard
March 21, 2018
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Transcript
Machine Learning: How It Can Help Your Business Szilárd Pafka,
PhD Chief Scientist, Epoch (USA) Microsoft Future Decoded, Budapest March 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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Source: Andrew Ng
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y = f(x) “Learn” f from data Source: Hastie etal,
ESL 2ed
Machine Learning linear/logistic regression decision trees neural networks support vector
machines random forests gradient boosting deep learning neural networks
Machine Learning linear/logistic regression (early 1900s/60s) decision trees (60s/80s) neural
networks (60s/80s) support vector machines (90s) random forests (90s) gradient boosting (90s) deep learning neural networks (2000s)
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data mining Source: Szilard Pafka
data science Source: Szilard Pafka
data science Source: Szilard Pafka
CRISP-DM, 1999
data $$$
How?
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Source: Andrew Ng
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Source: @iamdevloper (twitter)
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structured/tabular data: GBM (or RF) very small data: LR very
large sparse data: LR with SGD images/videos, speech: DL
structured/tabular data: GBM (or RF) very small data: LR very
large sparse data: LR with SGD images/videos, speech: DL better answer: it depends
structured/tabular data: GBM (or RF) very small data: LR very
large sparse data: LR with SGD images/videos, speech: DL better answer: it depends alternative answer: try them all
structured/tabular data: GBM (or RF) very small data: LR very
large sparse data: LR with SGD images/videos, speech: DL better answer: it depends alternative answer: try them all extra accuracy: combine them (ensembles)
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10x
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ML training: lots of CPU cores lots of RAM limited
time
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Source: Szilard Pafka
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Random forest GBM GBM + cross validation GBM + hyperparameter
tuning Logistic regression Neural Nets / Deep Learning Ensembles
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Backup Slides
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10x
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Source: Szilard Pafka: 10 Pitfalls in Data Science, LA Data
Science Meetup, February, 2014
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