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Total Size:
2.0 MB
Info Hash:
D6CB6B348E7E1889A4C01C0EA79962F7739DD574
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Added:
Aug. 6, 2025, 10:23 a.m.
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(Last updated: Aug. 6, 2025, 10:25 a.m.)
| File | Size |
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| Smets B. Mathematics of Neural Networks 2022.pdf | 2.0 MB |
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241.6 MB
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2023-06-02
| Uploaded by NoMercyReal | Size 241.6 MB | Health [ 4 /5 ] | Added 2023-06-02 |
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| Uploaded by Silmarillion | Size 241.6 MB | Health [ 0 /0 ] | Added 2023-06-02 |
NOTE
SOURCE: Smets B. Mathematics of Neural Networks 2022
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COVER
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MEDIAINFO
Textbook in PDF format The last decade has seen great experimental progress being made in machine learning, spearheaded by deep learning methods that make use of so-called deep neural networks. Many challenging, high-dimensional tasks that were previously beyond reach have become feasible with remarkably simple (in the algorithmic sense) techniques coupled with modern computational resources. Particularly in the fields of computer vision and natural language processing, deep learning is currently the go-to tool. Machine learning is usually positioned under the umbrella of artificial intelligence. While artificial intelligence is a fairly nebulous and broad term, the term machine learning refers specifically to algorithms that can improve their performance at a given task when presented with more data about the problem. Machine learning algorithms are used in a wide variety of applications such as speech recognition and computer vision, where it is difficult or impossible to develop conventional algorithms to perform the required task. These learning algorithms generally start from very general model that is then trained based in sample data to learn how to perform a specific task. When the underlying model being used is a (deep) neural network then we speak about deep learning
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