|
所在平台: Udemy |
课程主页: https://www.udemy.com/course/draft/6284585/
课程评论：没有评论
这份Coursera课程“认证生成式AI LLM：分主题考试备考”旨在帮助您全面准备认证生成式AI LLM考试。 **课程亮点：** * **分主题学习：** 课程将考试的关键主题逐一分解，帮助您建立在核心机器学习概念、AI工具和可信赖AI原则方面的专业知识。 * **海量练习题：** 提供超过350道练习题及答案，强化学习效果。 * **实战导向：** 结合目标性练习和真实世界场景，巩固您的技能。 * **NVIDIA技术聚焦：** 无论您是NVIDIA AI技术的新手还是希望深入了解，课程都将涵盖NVIDIA的AI技术和硬件。 **核心课程主题：** 1. **核心机器学习与AI知识 (30%)** * 机器学习和神经网络基础（监督、无监督、强化学习） * 常用算法（回归、分类、聚类）和神经网络基本概念（激活函数、前向/反向传播、损失函数） * 常见模型架构（CNN、RNN、GAN） * AI原理、NVIDIA硬件（GPU、Tensor Cores）和软件框架（CUDA、cuDNN） * NVIDIA深度学习解决方案（TensorRT、DeepStream）和生成式AI工具 2. **软件开发 (24%)** * Python编程技能（数据结构、控制流、代码优化） * AI和LLM库（TensorFlow, PyTorch, Hugging Face, NLP工具如SpaCy, NLTK） * 模型微调、部署和LLM集成（Docker, Kubernetes, NVIDIA Triton） 3. **实验设计 (22%)** * 有效实验设计、假设制定和A/B测试 * 数据预处理技巧（数据清洗、特征提取、降维、特征选择） 4. **数据分析与可视化 (14%)** * 统计分析、数据集汇总和趋势理解 * 数据挖掘和可视化方法（Matplotlib, Seaborn, Plotly），以及NLP数据和模型性能的可视化 5. **可信赖AI (10%)** * AI伦理原则（透明度、公平性、问责制、隐私） * 减少偏见的技术（公平性度量、模型审计） **重要提示：** 本课程是为“认证生成式AI LLM”考试提供的非官方备考资源，与NVIDIA无任何关联或认可。
This course is designed to prepare you thoroughly for the 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, 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 Certified Generative AI LLMs exam and is not affiliated with or endorsed by NVIDIA.
