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About Deep Learning in Textual Low-Data Regimes
In today’s fast-paced cybersecurity landscape, professionals are increasingly challenged by the vast volumes of cyber threat data, making it difficult to identify and mitigate threats effectively. Traditional clustering methods help in broadly categorizing threats but fall short when it comes to the fine-grained analysis necessary for precise threat management. Supervised Machine Learning offers a potential solution, but the rapidly changing nature of cyber threats renders static models ineffective and the creation of new models too labor-intensive. This book addresses these challenges by introducing innovative low-data regime methods that enhance the Machine Learning process with minimal labeled data. Deep Learning, a cornerstone of modern computational solutions, is revolutionizing fields ranging from decision-making and data analysis to data categorization. Cybersecurity, in particular, is one area that benefits from the capabilities of natural language processing (NLP) with Deep Learning. Integrating it into cybersecurity practices has become progressively essential in detecting, preventing, and mitigating sophisticated cyber threats and vulnerabilities prevalent today.
Detailed Information
| Author: | Markus Bayer |
|---|---|
| Pages: | 369 |
| Language: | English |
| File Size: | 11 |
| Format: | |
| Price: | FREE |
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