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
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