Securing LLMs

• Titel: Securing LLMs
• Titelzusatz: build secure LLM products using Python and Streamlit
• Mitwirkende: Pandit, Bhavishya [MitwirkendeR]
• Institutionen: O'Reilly (Firm), [Verlag]
• Ausgabe: [First edition].
• Verlagsort: [Sebastopol, California]
• Verlag: O'Reilly Media, Inc.
• E-Jahr: 2024
• Jahr: [2024]
• Umfang: 1 online resource (1 video file (54 min.))
• Illustrationen: sound, color.
• Fussnoten: Online resource; title from title details screen (O’Reilly, viewed August 19, 2024)
• Datenträger: Online-Ressource
• Sprache: eng
• Sach-SW: Génération automatique de texte
• Sach-SW: Python (Langage de programmation)
• Sach-SW: Intelligence artificielle ; Logiciels
• Sach-SW: Traitement automatique des langues naturelles
• Sach-SW: Instructional films
• Sach-SW: Nonfiction films
• Sach-SW: Internet videos
• Sach-SW: Films de formation
• Sach-SW: Films autres que de fiction
• Sach-SW: Vidéos sur Internet
• K10plus-PPN: 1900846748
• K10plus-PPN:
  Universitätsbibliothek
• Lokale URL UB: Zum Volltext
• Lokale URL UB: Bibliothek der Medizinischen Fakultät Mannheim der Universität Heidelberg
• Bibliothek/Idn: UW / m4573888225
• Lokale URL Inst.: Zum Volltext

Abstract

In this course you'll gain a deep understanding of the capabilities and security challenges of Large Language Models (LLMs). LLMs are at the forefront of technological innovation, but along with their tremendous potential they bring new risks that must be understood and addressed with practical solutions. By the end of this course, you'll possess the knowledge and skills required to work confidently with LLMs, tackle their challenges, address hallucinations, and secure these powerful tools by building security layers. Whether you're looking to enhance your AI expertise or stay at the forefront of technological advancements, this course is your gateway to mastering LLMs and unlocking their full potential by keeping them secure and reliable.