Search results for "Generative AI Modeling"
Items we need to order for you
R 1,761.30
R 763.00
R 1,401.32
R 559.36
R 815.00
Developer's Playbook for Large Language Model Security
Large language models (LLMs) are not just shaping the trajectory of AI, they're also unveiling a new era of security challenges. This practical book takes you straight to the heart of these threats. Author Steve Wilson, chief product officer at Exabeam, focuses exclusively on LLMs, eschewing generalized AI security to delve into the unique characteristics and vulnerabilities inherent in these models.Complete with collective wisdom gained from the creation of the OWASP Top 10 for LLMs list—a feat accomplished by more than 400 industry experts—this guide delivers real-world guidance and practical strategies to help developers and security teams grapple with the realities of LLM applications. Whether you're architecting a new application or adding AI features to an existing one, this book is your go-to resource for mastering the security landscape of the next frontier in AI.You'll learn:Why LLMs present unique security challengesHow to navigate the many risk conditions associated with using LLM technologyThe threat landscape pertaining to LLMs and the critical trust boundaries that must be maintainedHow to identify the top risks and vulnerabilities associated with LLMsMethods for deploying defenses to protect against attacks on top vulnerabilitiesWays to actively manage critical trust boundaries on your systems to ensure secure execution and risk minimization
R 1,824.00
R 1,744.18
Analog IC Placement Generation via Neural Networks from Unlabeled Data
In this book, innovative research using artificial neural networks (ANNs) is conducted to automate the placement task in analog integrated circuit layout design, by creating a generalized model that can generate valid layouts at push-button speed. Further, it exploits ANNs’ generalization and push-button speed prediction (once fully trained) capabilities, and details the optimal description of the input/output data relation. The description developed here is chiefly reflected in two of the system’s characteristics: the shape of the input data and the minimized loss function. In order to address the latter, abstract and segmented descriptions of both the input data and the objective behavior are developed, which allow the model to identify, in newer scenarios, sub-blocks which can be found in the input data. This approach yields device-level descriptions of the input topology that, for each device, focus on describing its relation to every other device in the topology. By means of these descriptions, an unfamiliar overall topology can be broken down into devices that are subject to the same constraints as a device in one of the training topologies. In the experimental results chapter, the trained ANNs are used to produce a variety of valid placement solutions even beyond the scope of the training/validation sets, demonstrating the model’s effectiveness in terms of identifying common components between newer topologies and reutilizing the acquired knowledge. Lastly, the methodology used can readily adapt to the given problem’s context (high label production cost), resulting in an efficient, inexpensive and fast model.
R 1,863.09
R 651.00
R 1,301.00
Definitive Guide to Google Vertex AI
Implement Machine Learning pipelines with Google Cloud Vertex AIKey Features* Understand the importance of an AI platform and MLOPs practices for Machine Learning projects* Learn Google Vertex AI tools and offerings for accelerating your end-to-end ML solution journey* Implement Vision, NLP and Recommendation based real-world ML models on Google Cloud PlatformBook DescriptionGoogle Cloud vertex AI is still relatively new product and has rapidly launched number of new features in last 6-12 months. There are no comprehensive books out there in the market right now which cover the newer features like Feature store, Metadata Store, Model registry, Managed Notebooks, Matching Engine etc.Since Google has now combined almost all different custom ML products under Vertex AI umbrella, there is a big push by Google to encourage more people to get certified on Vertex AI, thereby creating a huge demand for such books.What you will learn* Get quickly started on ML model development using Google Vertex AI* Learn how to manage datasets, artifacts and experiments* Develop no-code, low-code to Custom AI solution on Google Cloud* Learn to implement advanced model optimization techniques and tooling* Understand pre-build/turnkey AI solution offerings from Google* Learn to build and deploy custom ML models for real-world applicationsWho This Book Is ForIf you are a Machine Learning practitioner who wants to learn end-to-end ML solution development journey on Google Cloud Platform using MLOPs best practices and tools offered by Google Vertex AI, this book is for you. Starting from data storage, data management, this book takes you through Vertex AI offerings for building, experimenting, optimizing and deploying ML solutions in a fast and scalable way using Google best practices. It also covers topics related to scaling, monitoring and governing your ML Workloads with the help of MLOPs tooling on Google Cloud.
R 1,693.00
Theoretical and Computational Models of Word Learning
The process of learning words and languages may seem like an instinctual trait, inherent to nearly all humans from a young age. However, a vast range of complex research and information exists in detailing the complexities of the process of word learning. Theoretical and Computational Models of Word Learning: Trends in Psychology and Artificial Intelligence strives to combine cross-disciplinary research into one comprehensive volume to help readers gain a fuller understanding of the developmental processes and influences that makeup the progression of word learning. Blending together developmental psychology and artificial intelligence, this publication is intended for researchers, practitioners, and educators who are interested in language learning and its development as well as computational models formed from these specific areas of research.
R 6,827.56
Modern Approaches in Machine Learning and Cognitive Science: A Walkthrough
This book discusses various machine learning & cognitive science approaches, presenting high-throughput research by experts in this area. Bringing together machine learning, cognitive science and other aspects of artificial intelligence to help provide a roadmap for future research on intelligent systems, the book is a valuable reference resource for students, researchers and industry practitioners wanting to keep abreast of recent developments in this dynamic, exciting and profitable research field. It is intended for postgraduate students, researchers, scholars and developers who are interested in machine learning and cognitive research, and is also suitable for senior undergraduate courses in related topics. Further, it is useful for practitioners dealing with advanced data processing, applied mathematicians, developers of software for agent-oriented systems and developers of embedded and real-time systems.
R 5,188.81
R 1,563.00
R 1,238.00
