Upgrade to Pro
— share decks privately, control downloads, hide ads and more …
Speaker Deck
Sign up for free
Menu
Search
Features
All features
Private URLs
Password Protection
Custom URLS
Scheduled publishing
Remove Branding
Restrict embedding
Deck Collections
Notes
Features
All features
Private URLs
Password Protection
Custom URLS
Scheduled publishing
Remove Branding
Restrict embedding
Deck Collections
Notes
Explore
Featured decks
Featured speakers
Programming
Technology
Storyboards
Explore
Featured decks
Featured speakers
Programming
Technology
Storyboards
Pricing
Search
Sign in
Sign up for free
Machine Learning Trends in 2017 - Budapest Data...
Search
szilard
December 19, 2017
150
0
Share
Embed
Copy iframe code
Copy JS code
Copy link
Start on current slide
Machine Learning Trends in 2017 - Budapest Data Christmas - Dec 2017
szilard
December 19, 2017
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
260
Make Machine Learning Boring Again: Best Practices for Using Machine Learning in Businesses - Albuquerque Machine Learning Meetup (Online) - Aug 2020
szilard
0
180
Better than Deep Learning: Gradient Boosting Machines (GBM) - eRum conference - invited talk - June 2020
szilard
0
160
Gradient Boosting Machines (GBM): From Zero to Hero (with R and Python Code) - LA Data Science Meetup - February 2020
szilard
0
150
A Random Walk in Data Science and Machine Learning in Practice - CEU, Business Analytics Masters - Budapest, Febr 2020
szilard
0
350
Better than My Meetup/Conference Talks: Going Deeper in Various GBM Topics - GBM Advanced Workshop - Budapest, Nov 2019
szilard
0
130
Gradient Boosting Machines (GBM): From Zero to Hero (with R and Python Code) - Budapest BI Forum, Budapest, Nov 2019
szilard
0
180
Make Machine Learning Boring Again: Best Practices for Using Machine Learning in Businesses - LA Data Science Meetup - Playa Vista, August 2019
szilard
0
170
Better than Deep Learning: Gradient Boosting Machines (GBM) / 2019 edition - Budapest R and Data Science Meetups - Budapest, June 2019
szilard
0
150
Featured
See All Featured
JavaScript: Past, Present, and Future - NDC Porto 2020
reverentgeek
52
6.1k
Lessons Learnt from Crawling 1000+ Websites
charlesmeaden
PRO
1
1.6k
Why Your Marketing Sucks and What You Can Do About It - Sophie Logan
marketingsoph
0
430
How to build a perfect <img>
jonoalderson
1
6.1k
Documentation Writing (for coders)
carmenintech
77
5.6k
Sam Torres - BigQuery for SEOs
techseoconnect
PRO
0
580
The Success of Rails: Ensuring Growth for the Next 100 Years
eileencodes
47
8.4k
Imperfection Machines: The Place of Print at Facebook
scottboms
270
14k
The Curious Case for Waylosing
cassininazir
1
570
How to Build an AI Search Optimization Roadmap - Criteria and Steps to Take #SEOIRL
aleyda
1
2.2k
Practical Tips for Bootstrapping Information Extraction Pipelines
honnibal
25
2.1k
Claude Code どこまでも/ Claude Code Everywhere
nwiizo
68
59k
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