Chai Time Data Science
En podcast af Sanyam Bhutani
Kategorier:
149 Episoder
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Top Kagglers Panel on Best Practises for Training Models
Udgivet: 22.1.2023 -
Announcement + Amed Coulibaly | How to become Kaggle Competitions Grandmaster | #159
Udgivet: 29.12.2022 -
Jeremy Howard interviews Kaggle Grandmaster Sanyam Bhutani | #150
Udgivet: 23.3.2022 -
Kathleen Walch, Ronald Schmelzer: AI Today Podcast, Creating AI Content: #137
Udgivet: 6.10.2021 -
ACM RecSys Winning Solution: Benedikt Schifferer, Bo Liu, Chris Deotte, Even Oldridge #136
Udgivet: 31.7.2021 -
"SentDex", Harrison Kinsley: YouTube, Entrepreneurship and NNFS.io #135
Udgivet: 23.7.2021 -
Clair Sullivan: Graphs and the Neo4J Ecosystem #134
Udgivet: 20.7.2021 -
Emil Wallner: Art & ML, Being Internet Taught, Creating ML Content #133
Udgivet: 7.1.2021 -
Andrada Olteanu: Learning Data Science, Journey to becoming Kaggle Master #132
Udgivet: 3.1.2021 -
Laura Leal Taixé: Computer Vision & Research at the Dynamic Vision & Learning Group #131
Udgivet: 31.12.2020 -
William Falcon: The PyTorch Lightning Story #130
Udgivet: 27.12.2020 -
Katy Warr: Fooling AI, Strengthening Deep Neural Networks Book, Adversarial Attacks #129
Udgivet: 24.12.2020 -
Laura Fink: Journey to becoming Kaggle Kernels Grandmaster #128
Udgivet: 20.12.2020 -
Barr Moses: Data Reliability, Data Downtime, MonteCarlo Data #127
Udgivet: 17.12.2020 -
David Luebke: Graphics Research at NVIDIA, Training GANs with Limited Data #126
Udgivet: 13.12.2020 -
Torsten Sattler: CV, Mixed Reality, Localisation & Robotics #125
Udgivet: 10.12.2020 -
Zachary Mueller: Learning, Applying and contributing to Fastai #124
Udgivet: 6.12.2020 -
Arsha Nagrani: Multi-Modal Research, Speaker Diarisation, VoxCeleb #123
Udgivet: 3.12.2020 -
Ekaterina Kochmar: Automated Language Teaching & Assessment, NLP, Korbit.ai #122
Udgivet: 29.11.2020 -
Richard Craib: The Numerai Story, Building the World's last Hedge Fund #121
Udgivet: 26.11.2020
Chai Time Data Science show is a series where Sanyam Bhutani interviews his Data Science Heroes: Practitioners, Kagglers & Researchers about all things Data Science