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
A Random Walk in Data Science and Machine Learn...
Search
Sponsored
·
Your Podcast. Everywhere. Effortlessly.
Share. Educate. Inspire. Entertain. You do you. We'll handle the rest.
→
szilard
February 12, 2020
340
0
Share
Embed
Copy iframe code
Copy JS code
Copy link
Start on current slide
A Random Walk in Data Science and Machine Learning in Practice - CEU, Business Analytics Masters - Budapest, Febr 2020
szilard
February 12, 2020
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
240
Make Machine Learning Boring Again: Best Practices for Using Machine Learning in Businesses - Albuquerque Machine Learning Meetup (Online) - Aug 2020
szilard
0
170
Better than Deep Learning: Gradient Boosting Machines (GBM) - eRum conference - invited talk - June 2020
szilard
0
150
Gradient Boosting Machines (GBM): From Zero to Hero (with R and Python Code) - LA Data Science Meetup - February 2020
szilard
0
140
Better than My Meetup/Conference Talks: Going Deeper in Various GBM Topics - GBM Advanced Workshop - Budapest, Nov 2019
szilard
0
110
Gradient Boosting Machines (GBM): From Zero to Hero (with R and Python Code) - Budapest BI Forum, Budapest, Nov 2019
szilard
0
170
Make Machine Learning Boring Again: Best Practices for Using Machine Learning in Businesses - LA Data Science Meetup - Playa Vista, August 2019
szilard
0
160
Better than Deep Learning: Gradient Boosting Machines (GBM) / 2019 edition - Budapest R and Data Science Meetups - Budapest, June 2019
szilard
0
140
Better than Deep Learning: Gradient Boosting Machines (GBM) / 2019 edition - LA R Meetup - Santa Monica, May 2019
szilard
0
38
Featured
See All Featured
Jess Joyce - The Pitfalls of Following Frameworks
techseoconnect
PRO
1
310
The AI Revolution Will Not Be Monopolized: How open-source beats economies of scale, even for LLMs
inesmontani
PRO
3
3.6k
Gemini Prompt Engineering: Practical Techniques for Tangible AI Outcomes
mfonobong
2
470
Why You Should Never Use an ORM
jnunemaker
PRO
61
9.9k
Responsive Adventures: Dirty Tricks From The Dark Corners of Front-End
smashingmag
254
22k
Conquering PDFs: document understanding beyond plain text
inesmontani
PRO
4
2.9k
More Than Pixels: Becoming A User Experience Designer
marktimemedia
3
470
Designing for Timeless Needs
cassininazir
1
400
[RailsConf 2023 Opening Keynote] The Magic of Rails
eileencodes
31
10k
Navigating Algorithm Shifts & AI Overviews - #SMXNext
aleyda
1
1.5k
Digital Ethics as a Driver of Design Innovation
axbom
PRO
1
350
Data-driven link building: lessons from a $708K investment (BrightonSEO talk)
szymonslowik
1
1.2k
Transcript
A Random Walk in Data Science and Machine Learning in
Practice Szilard Pafka, PhD Chief Scientist, Epoch (USA) CEU, Business Analytics Masters Budapest, Febr 2020
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
None
CRISP-DM, 1999
None
None
None
None
None
None
None
None
None
None
None
None
None
None
None
None
None
None
None
None
None
None
None
None
None
None
None
None
None
None
None
None
None
None
None
None
None
None
None
None
Better than Deep Learning: Gradient Boosting Machines (GBM) - 2019
Updated Edition Szilard Pafka, PhD Chief Scientist, Epoch (USA) Barcelona, Los Angeles, Budapest, Berlin (confs/meetups) 2019
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
Source: Andrew Ng
Source: Andrew Ng
Source: Andrew Ng
None
None
None
None
Source: https://twitter.com/iamdevloper/
None
None
...
None
None
None
http://lowrank.net/nikos/pubs/empirical.pdf http://www.cs.cornell.edu/~alexn/papers/empirical.icml06.pdf
http://lowrank.net/nikos/pubs/empirical.pdf http://www.cs.cornell.edu/~alexn/papers/empirical.icml06.pdf
None
None
None
None
None
None
None
None
None
None
None
None
None
None
None
None
Source: Hastie etal, ESL 2ed
Source: Hastie etal, ESL 2ed
Source: Hastie etal, ESL 2ed
Source: Hastie etal, ESL 2ed
None
None
None
None
None
None
10x
None
None
None
10x
10x
None
None
None
None
None
None
None
None
None
None
None
None
None
None
None
None
None
None
None
None
None
None
None
None
None
None
None
None
None
None
None
None
None
None
None
None
None
None
None
None
None
http://www.jmlr.org/papers/volume13/bergstra12a/bergstra12a.pdf
http://www.argmin.net/2016/06/20/hypertuning/
None
None
None
None
None
None
None
CPU 1
CPU 1 CPU 2
CPU 1 CPU 2
CPU 1 CPU 2
CPU 1 CPU 2
None
None
None
None
None
None
None
*
None
no-one is using this crap
(2018)
(2018)
None
Source: https://www.linkedin.com/pulse/winning-solution-kaggledays-2019-competition-san-francisco-mark-peng/
Source: https://www.linkedin.com/pulse/winning-solution-kaggledays-2019-competition-san-francisco-mark-peng/
Source: https://www.linkedin.com/pulse/winning-solution-kaggledays-2019-competition-san-francisco-mark-peng/
Source: https://www.linkedin.com/pulse/winning-solution-kaggledays-2019-competition-san-francisco-mark-peng/
Source: https://www.linkedin.com/pulse/winning-solution-kaggledays-2019-competition-san-francisco-mark-peng/
Source: https://www.linkedin.com/pulse/winning-solution-kaggledays-2019-competition-san-francisco-mark-peng/
None
More:
None
A Few More Thoughts
None
None
None
None
None
None
None
None
None
None
None
None
None