Anthony Goldbloom — How to Win Kaggle Competitions
Anthony Goldbloom is the founder and CEO of Kaggle. In 2011 & 2012, Forbes Magazine named Anthony as one of the 30 under 30 in technology. In 2011, Fast Company featured him as one of the innovative thinkers who are changing the future of business.
He and Lukas discuss the differences in strategies that do well in Kaggle competitions vs academia vs in production. They discuss his 2016 Ted talk through the lens of 2020, frameworks, and languages.
Topics Discussed:
0:00 Sneak Peek
0:20 Introduction
0:45 methods used in kaggle competitions vs mainstream academia
2:30 What are people doing to win competitions/feature engineering
3:55 Kaggle Competitions now vs 10 years ago
8:35 Data augmentation strategies
10:06 Overfitting in Kaggle Competitions
12:53 How to not overfit
14:11 Kaggle competitions vs the real world
18:15 Getting into ML through Kaggle
22:03 Other Kaggle products
25:48 Favorite under appreciated kernel or dataset
28:27 Python & R
32:03 Frameworks
35:15 2016 Ted talk though the 2020 frame
37:54 Reinforcement Learning
38:43 What’s the topic in ML that people don’t talk about enough?
42:02 Where are the biggest bottlenecks in deploying ML software?
Check out Kaggle: https://www.kaggle.com/
Follow Anthony on Twitter: https://twitter.com/antgoldbloom
Watch his 2016 Ted Talk: https://www.ted.com/talks/anthony_goldbloom_the_jobs_we_ll_lose_to_machines_and_the_ones_we_won_t
Visit our podcasts homepage for transcripts and more episodes!
www.wandb.com/podcast
🎙 Get our podcasts on these platforms:
Soundcloud: http://wandb.me/soundcloud
Apple Podcasts: http://wandb.me/apple-podcasts
Spotify: http://wandb.me/spotify
Google: http://wandb.me/gd_google
YouTube: http://wandb.me/youtube
We started Weights and Biases to build tools for Machine Learning practitioners because we care a lot about the impact that Machine Learning can have in the world and we love working in the trenches with the people building these models. One of the most fun things about these building tools has been the conversations with these ML practitioners and learning about the interesting things they’re working on. This process has been so fun that we wanted to open it up to the world in the form of our new podcast called Gradient Dissent. We hope you have as much fun listening to it as we had making it!
Weights and Biases:
We’re always free for academics and open source projects. Email carey@wandb.com with any questions or feature suggestions.
* Blog: https://www.wandb.com/articles
* Gallery: See what you can create with W&B - https://app.wandb.ai/gallery
* Join our community of ML practitioners working on interesting problems - https://www.wandb.com/ml-community
Host: Lukas Biewald - https://twitter.com/l2k
Producer: Lavanya Shukla - https://twitter.com/lavanyaai
Editor: Cayla Sharp - http://caylasharp.com/
Видео Anthony Goldbloom — How to Win Kaggle Competitions канала Weights & Biases
He and Lukas discuss the differences in strategies that do well in Kaggle competitions vs academia vs in production. They discuss his 2016 Ted talk through the lens of 2020, frameworks, and languages.
Topics Discussed:
0:00 Sneak Peek
0:20 Introduction
0:45 methods used in kaggle competitions vs mainstream academia
2:30 What are people doing to win competitions/feature engineering
3:55 Kaggle Competitions now vs 10 years ago
8:35 Data augmentation strategies
10:06 Overfitting in Kaggle Competitions
12:53 How to not overfit
14:11 Kaggle competitions vs the real world
18:15 Getting into ML through Kaggle
22:03 Other Kaggle products
25:48 Favorite under appreciated kernel or dataset
28:27 Python & R
32:03 Frameworks
35:15 2016 Ted talk though the 2020 frame
37:54 Reinforcement Learning
38:43 What’s the topic in ML that people don’t talk about enough?
42:02 Where are the biggest bottlenecks in deploying ML software?
Check out Kaggle: https://www.kaggle.com/
Follow Anthony on Twitter: https://twitter.com/antgoldbloom
Watch his 2016 Ted Talk: https://www.ted.com/talks/anthony_goldbloom_the_jobs_we_ll_lose_to_machines_and_the_ones_we_won_t
Visit our podcasts homepage for transcripts and more episodes!
www.wandb.com/podcast
🎙 Get our podcasts on these platforms:
Soundcloud: http://wandb.me/soundcloud
Apple Podcasts: http://wandb.me/apple-podcasts
Spotify: http://wandb.me/spotify
Google: http://wandb.me/gd_google
YouTube: http://wandb.me/youtube
We started Weights and Biases to build tools for Machine Learning practitioners because we care a lot about the impact that Machine Learning can have in the world and we love working in the trenches with the people building these models. One of the most fun things about these building tools has been the conversations with these ML practitioners and learning about the interesting things they’re working on. This process has been so fun that we wanted to open it up to the world in the form of our new podcast called Gradient Dissent. We hope you have as much fun listening to it as we had making it!
Weights and Biases:
We’re always free for academics and open source projects. Email carey@wandb.com with any questions or feature suggestions.
* Blog: https://www.wandb.com/articles
* Gallery: See what you can create with W&B - https://app.wandb.ai/gallery
* Join our community of ML practitioners working on interesting problems - https://www.wandb.com/ml-community
Host: Lukas Biewald - https://twitter.com/l2k
Producer: Lavanya Shukla - https://twitter.com/lavanyaai
Editor: Cayla Sharp - http://caylasharp.com/
Видео Anthony Goldbloom — How to Win Kaggle Competitions канала Weights & Biases
Показать
Комментарии отсутствуют
Информация о видео
Другие видео канала
W&B Deep Learning Salon - Robert Nishihara & Rok NovoselAI's long-term impact on software engineeringUsing physics as training data for foundation modelsChai Time Kaggle Talk with Mark TenenholtzLukas x Amjad: Cool Projects for the Replit x Weights & Biases ML HackathonToyota Research Institute on Experiment Tracking with Weights & BiasesMars rovers and machine learning with NASA JPL's Chris MattmannNN Basics in Keras: Tensors, Tensor Operations & MoreAn Interview With Jerome Pesenti For Gradient DissentW&B Fastbook Reading Group — 10. Movie recommendation using fastaiSamba-1, enterprise grade open source AIAlligator Pears, Random Variables, and Gradient DescentTrends in Machine Learning Startups & InvestingTrack Your Keras Machine Learning Experiments with Weights & BiasesDeep Dive into Document Parsing Mastery: Chapter 16Pieter Abbeel — Robotics, Startups, and Robotics Startups안녕하세요 Korea, from W&BNavigating the New Era of AI with Pinecone CEO Edo LibertyWeights & Biases at OpenAICall for Reproducing PapersExploring W&B workspace