NVIDIA-Certified Associate - Generative AI LLMs: A Complete Guide to Certification and Deployment
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Paperback
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0.77 kg
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Amazon
USA
- NVIDIA-Certified Associate - Generative AI LLMs: A Complete Guide to Certification and Deployment is an in-depth resource designed for professionals looking to master the skills necessary to become certified as an NVIDIA-Certified Associate in Generative AI and Large Language Models (LLMs). This comprehensive guide covers everything from foundational concepts to advanced techniques, focusing on the practical application of AI tools and deployment strategies.The book begins with an overview of machine learning and neural networks, providing a strong understanding of how generative AI models, including LLMs, GANs, and Diffusion Models, are built and trained. It introduces key concepts like activation functions, loss functions, and backpropagation, ensuring readers understand the mechanics behind these models.As readers progress, the book dives into NVIDIA’s powerful frameworks and platforms. It covers tools like cuDF for data preparation, cuML for machine learning algorithms, and TensorRT for model optimization, ensuring that readers can harness the full power of NVIDIA GPUs for efficient AI model training and inference.The guide provides practical insights into building, training, and deploying models using real-world datasets, including image and text classification, convolutional neural networks (CNNs), and natural language processing (NLP) applications like BERT. It also includes hands-on exercises that cover data augmentation, transfer learning, and deployment techniques, along with strategies for optimizing model performance and maintaining high efficiency.Finally, the book prepares readers for the certification exam, with targeted content that aligns with the official NVIDIA-Certified Associate exam objectives. By the end of the book, readers will have the knowledge and skills to confidently implement, optimize, and deploy generative AI models using NVIDIA tools in both cloud and edge environments.
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