Upgrade to Pro
— share decks privately, control downloads, hide ads and more …
Speaker Deck
Features
Speaker Deck
PRO
Sign in
Sign up for free
Search
Search
Machine Learning Trends in 2017 - Budapest Data Christmas - Dec 2017
Search
szilard
December 19, 2017
0
110
Machine Learning Trends in 2017 - Budapest Data Christmas - Dec 2017
szilard
December 19, 2017
Tweet
Share
More Decks by szilard
See All by szilard
Gradient Boosting Machines (GBM): From Zero to Hero (with R and Python Code) - Data Con LA - Oct 2020
szilard
0
120
Make Machine Learning Boring Again: Best Practices for Using Machine Learning in Businesses - Albuquerque Machine Learning Meetup (Online) - Aug 2020
szilard
0
89
Better than Deep Learning: Gradient Boosting Machines (GBM) - eRum conference - invited talk - June 2020
szilard
0
83
Gradient Boosting Machines (GBM): From Zero to Hero (with R and Python Code) - LA Data Science Meetup - February 2020
szilard
0
76
A Random Walk in Data Science and Machine Learning in Practice - CEU, Business Analytics Masters - Budapest, Febr 2020
szilard
0
270
Better than My Meetup/Conference Talks: Going Deeper in Various GBM Topics - GBM Advanced Workshop - Budapest, Nov 2019
szilard
0
53
Gradient Boosting Machines (GBM): From Zero to Hero (with R and Python Code) - Budapest BI Forum, Budapest, Nov 2019
szilard
0
130
Make Machine Learning Boring Again: Best Practices for Using Machine Learning in Businesses - LA Data Science Meetup - Playa Vista, August 2019
szilard
0
98
Better than Deep Learning: Gradient Boosting Machines (GBM) / 2019 edition - Budapest R and Data Science Meetups - Budapest, June 2019
szilard
0
77
Featured
See All Featured
The Mythical Team-Month
searls
217
43k
Done Done
chrislema
179
15k
Large-scale JavaScript Application Architecture
addyosmani
506
110k
Distributed Sagas: A Protocol for Coordinating Microservices
caitiem20
325
21k
Adopting Sorbet at Scale
ufuk
71
8.8k
How GitHub Uses GitHub to Build GitHub
holman
471
290k
Easily Structure & Communicate Ideas using Wireframe
afnizarnur
189
16k
Writing Fast Ruby
sferik
623
60k
Understanding Cognitive Biases in Performance Measurement
bluesmoon
18
1.2k
Clear Off the Table
cherdarchuk
89
320k
Building a Scalable Design System with Sketch
lauravandoore
458
32k
ピンチをチャンスに:未来をつくるプロダクトロードマップ #pmconf2020
aki_iinuma
90
47k
Transcript
Machine Learning Trends in 2017 Szilárd Pafka, PhD Chief Scientist,
Epoch Budapest Data Christmas December 2017
None
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
None
Source: Andrew Ng
None
None
None
None
...
None
None
None
None
None
None
None
None
None
10x
10x
None
None
None
None
None
None
None
1M: CPU cache effects
(lightgbm 10M)
16 cores vs 1: 16 cores:
None
None
Aggregation 100M rows 1M groups Join 100M rows x 1M
rows time [s] time [s]
Aggregation 100M rows 1M groups Join 100M rows x 1M
rows time [s] time [s]
None
None
None
None
None
me @confs / talks :: 2017