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Total Size:
86.0 MB
Info Hash:
01D5C45CB116C620036DB4CF200FC78638B796BF
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Added:
Sept. 21, 2025, 11:57 a.m.
Stats:
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(Last updated: Sept. 21, 2025, noon)
| File | Size |
|---|---|
| Neumann G. Machine Learning - Foundations and Algorithms 2025.pdf | 86.0 MB |
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86.0 MB
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2025-09-21
| Uploaded by andryold1 | Size 86.0 MB | Health [ 33 /12 ] | Added 2025-09-21 |
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899.7 MB
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2025-06-05
| Uploaded by Yurievij | Size 899.7 MB | Health [ 29 /40 ] | Added 2025-06-05 |
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38.4 MB
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2023-07-01
| Uploaded by indexFroggy | Size 38.4 MB | Health [ 62 /10 ] | Added 2023-07-01 |
NOTE
SOURCE: Neumann G. Machine Learning - Foundations and Algorithms 2025
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COVER
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MEDIAINFO
Textbook in PDF format Algorithms that can improve their performance using training data Typically we have a large number of parameters Learned from data Useful if: No expert knowledge available: industrial/manufacturing control, mass spectrometer analysis, drug design, astronomic discovery Black-box expert knowledge: face/handwriting/speech recognition, driving a car, flying a plane Fast changing phenomena: credit scoring, financial modeling, diagnosis, fraud detection Customization/personalization: personalized news reader, movie/book recommendation Why do we see this “explosion” now? More training data More computation power New algorithms (deep learning, generative models)… – But: same principles as from 40 years ago are still in use "when you go from 10,000 training examples to 10 billion training examples, it all starts to work. Data trumps everything.”
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