Uploaded April 2024 | Updated September 2026, 2 weeks ago
Sebastian's books: sebastianraschka.com/books
REFERENCES:
1. Link to the code on GitHub: github.com/rasbt/MachineLearning-QandAI-book/tree/main/supplementary/q10-random-sources
2. Link to the book mentioned at the end of the video: nostarch.com/machine-learning-q-and-ai
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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To support this channel, please consider purchasing a copy of my books: sebastianraschka.com/books
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twitter.com/rasbt
linkedin.com/in/sebastianraschka
magazine.sebastianraschka.com
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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
Sebastian's books: sebastianraschka.com/books
REFERENCES:
1. Link to the code on GitHub: github.com/rasbt/MachineLearning-QandAI-book/tree/main/supplementary/q10-random-sources
2. Link to the book mentioned at the end of the video: nostarch.com/machine-learning-q-and-ai
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.
---
To support this channel, please consider purchasing a copy of my books: sebastianraschka.com/books
---
twitter.com/rasbt
linkedin.com/in/sebastianraschka
magazine.sebastianraschka.com
---
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
