Managing Sources of Randomness When Training Deep Neural Networks
Sebastian's books: https://sebastianraschka.com/books/
REFERENCES:
1. Link to the code on GitHub: https://github.com/rasbt/MachineLearn...
2. Link to the book mentioned at the end of the video: https://nostarch.com/machine-learning...
DESCRIPTION:
In this video, we managing common sources of randomness when training deep neural networks. We cover sources of randomness, including model weight initialization, dataset sampling and shuffling, nondeterministic algorithms, runtime algorithm differences, hardware and driver variations, and generative AI sampling.
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OUTLINE:
00:00 – Introduction
01:14 – 1. Model Weight Initialization
04:28 – 2. Dataset Sampling and Shuffling
07:45 – 3. Nondeterministic Algorithms
11:13 – 4. Different Runtime Algorithms
14:30 – 5. Hardware and Drivers
15:39 – 6. Randomness and Generative AI
20:56 – Recap
22:34 – Surprise
Watch video Managing Sources of Randomness When Training Deep Neural Networks online without registration, duration hours minute second in high quality. This video was added by user Sebastian Raschka 16 April 2024, don't forget to share it with your friends and acquaintances, it has been viewed on our site 2,499 once and liked it 96 people.