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所在平台: Udemy |
课程主页: https://www.udemy.com/course/nvidia-certified-generative-ai-llms-topic-wise-exam-prep/
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**课程名称：NVIDIA认证生成式AI大语言模型（LLM）考前复习** **课程概述：** 本课程旨在帮助您全面准备NVIDIA认证生成式AI大语言模型（LLM）考试。通过将考试内容细分为各个关键主题，并提供超过350道练习题及答案，课程将帮助您深入掌握核心机器学习概念、NVIDIA AI工具以及可信赖AI原则。无论您是NVIDIA AI技术的新手，还是希望加深理解，本课程的每个部分都将通过有针对性的练习和真实场景强化您的技能。 **课程涵盖的主题：** * **核心机器学习与AI知识 (30%)** * 深入学习机器学习和神经网络基础，包括监督学习、无监督学习和强化学习。 * 掌握基本算法（回归、分类、聚类）和神经网络基础（激活函数、前向/后向传播、损失函数）。 * 探索常见架构，如CNN、RNN和GAN。 * 了解AI原则、NVIDIA硬件（GPU、Tensor Cores）和软件框架（CUDA、cuDNN）。 * 熟悉NVIDIA深度学习解决方案，如TensorRT和DeepStream，以及生成式AI工具。 * **软件开发 (24%)** * 提高Python编程技能，重点关注数据结构、控制流以及编写清晰、优化的代码。 * 熟练掌握核心AI和LLM库，如TensorFlow、PyTorch、Hugging Face和NLP工具（SpaCy、NLTK）。 * 通过Docker、Kubernetes和NVIDIA Triton，提升模型微调、部署和LLM集成技能，实现高效的模型服务。 * **实验设计 (22%)** * 学习设计有效的实验、提出假设，并运用A/B测试评估模型性能。 * 精进数据预处理技能，涵盖数据清洗、特征提取、降维以及NLP和图像数据的特征选择技术。 * **数据分析与可视化 (14%)** * 提升统计分析能力，学会总结大型数据集，理解数据趋势。 * 探索数据挖掘和可视化方法，使用Matplotlib、Seaborn和Plotly，并学习可视化NLP数据和模型性能。 * **可信赖AI (10%)** * 学习伦理AI原则，关注透明度、公平性、问责制和隐私。 * 掌握降低偏见的技术，包括公平性指标和模型审计，以确保AI解决方案的公平性。 **免责声明：** 本文是NVIDIA认证生成式AI LLMs考试的非官方复习资源，与NVIDIA无关，也未获得NVIDIA的认可。
This course is designed to prepare you thoroughly for the NVIDIA Certified Generative AI LLMs exam by breaking down each key topic and providing over 350 practice questions and answers. With topic-wise organization, you'll build expertise in core machine learning concepts, NVIDIA's AI tools, and principles of trustworthy AI. Whether you're new to NVIDIA's AI technologies or aiming to deepen your understanding, each section reinforces your skills through targeted practice and real-world scenarios.Course Topics CoveredCore Machine Learning and AI Knowledge (30%)Dive into the fundamentals of machine learning and neural networks, including supervised, unsupervised, and reinforcement learning.Master essential algorithms (regression, classification, clustering) and neural network basics like activation functions, forward/backward propagation, and loss functions.Explore common architectures such as CNNs, RNNs, and GANs.Get insights into AI principles, NVIDIA hardware (GPUs, Tensor Cores), and software frameworks like CUDA and cuDNN.Understand NVIDIA's deep learning solutions, such as TensorRT and DeepStream, and generative AI tools.Software Development (24%)Strengthen your Python programming skills with a focus on data structures, control flow, and writing clean, optimized code.Gain proficiency with essential AI and LLM libraries like TensorFlow, PyTorch, Hugging Face, and NLP tools (SpaCy, NLTK).Develop skills for model fine-tuning, deployment, and LLM integration, using Docker, Kubernetes, and NVIDIA Triton for efficient model serving.Experimentation (22%)Learn to design effective experiments, formulate hypotheses, and use A/B testing to evaluate model performance.Refine your data preprocessing skills, covering techniques for data cleaning, feature extraction, dimensionality reduction, and feature selection for NLP and image data.Data Analysis and Visualization (14%)Build expertise in statistical analysis, summarizing large datasets, and understanding trends.Explore data mining and visualization methods using Matplotlib, Seaborn, and Plotly, and learn to visualize NLP data and model performance.Trustworthy AI (10%)Study ethical AI principles, focusing on transparency, fairness, accountability, and privacy.Learn techniques to minimize bias, including fairness metrics and model auditing to ensure equitable AI solutions.Disclaimer:This is an unofficial preparation resource for the NVIDIA Certified Generative AI LLMs exam and is not affiliated with or endorsed by NVIDIA.
