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所在平台: Udemy |
课程主页: https://www.udemy.com/course/ace-generative-ai-interview-6-practice-tests-400-qa/
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课程名称：掌握生成式人工智能面试: 400+ 专家级问答精通 课程概述：本课程旨在帮助学员在生成式人工智能面试中脱颖而出，通过全面的练习课程，提供6份完整的模拟测试，其中包含超过400个概念性和情景性问题，涵盖生成式人工智能的核心原则和高级概念。课程设计旨在帮助学员理解基础数学模型、实际应用及行业案例，增强对关键主题的理解并提升自信心。通过针对性的练习，您将深化对核心生成模型（包括GANs、VAEs、自回归模型和扩散模型）的理解，同时应对模型训练、评估和伦理考虑等实际挑战。 您将学到的内容： - 生成式人工智能的关键概念和数学基础 - GANs、VAEs、自回归模型和扩散模型的架构差异及应用 - 基于Transformer的生成模型，包括GPT和DALL·E - 模型训练、评估与优化的最佳实践 - 伦理影响和负责任的人工智能实践 课程结构： 1. 生成式人工智能概述与基础 - 生成模型与判别模型的定义和核心概念 - 历史背景和关键里程碑（如玻尔兹曼机、VAEs、GANs） - 应用：文本、图像、音频、合成数据等 - 主要优势与挑战（如创造性、偏见、计算成本） 2. 数学和统计基础 - 概率分布和潜在变量 - 贝叶斯推断基础：先验、似然、后验 - 信息论概念：熵、KL散度、互信息 3. 核心生成模型家族 - GANs：生成器-判别器架构、训练挑战、变种（DCGAN、WGAN、StyleGAN） - VAEs：编码器-解码器架构、ELBO目标、与GAN的权衡 - 自回归模型：PixelCNN、PixelRNN、直接概率估计 - 归一化流：可逆变换，实际应用 4. 基于Transformer的生成模型 - 自注意力机制，编码器-解码器与仅解码器模型 - 大型语言模型：GPT系列（GPT-2、GPT-3、GPT-4）及训练策略 - 文本到图像模型：DALL·E、Stable Diffusion、挑战和伦理问题 5. 生成模型的训练 - 数据收集和预处理以确保一致输入 - 优化和损失函数（对抗损失、重建损失） - 硬件和软件生态系统（TensorFlow、PyTorch） - 实用技术：超参数调优、梯度惩罚、迁移学习 6. 评估与指标 - 定量指标：生成分数（IS）、Fréchet生成距离（FID）、困惑度 - 定性评估：人类感知测试、用户研究 - 测量语义正确性和创造性的挑战 7. 伦理、社会和法律影响 - 训练数据中的偏见及缓解策略 - 内容真实性、深度伪造和水印问题 - AI生成内容的版权问题和所有权 - 负责任的部署和透明框架 8. 高级主题与最新研究 - 扩散模型：去噪扩散模型及应用 - 多模态人工智能：跨模态检索与生成 - 生成模型的强化学习：受控生成策略 - 自监督学习：对比学习、掩码自编码 - 未来趋势：实时3D生成、基础模型 本课程将为您提供结构化和深入的生成式人工智能理解，使您具备应对实际挑战和在技术面试中成功所需的知识与信心。
Prepare to ace your Generative AI interviews with this comprehensive practice course. This course provides 6 full-length practice tests with over 400 conceptual and scenario-based questions covering the core principles and advanced concepts of Generative AI. Designed to help you understand the underlying mathematical models, practical applications, and industry use cases, this course will strengthen your grasp of key topics and boost your confidence.Through targeted practice, you will enhance your understanding of core generative models, including GANs, VAEs, autoregressive models, and diffusion models, while also tackling real-world challenges in model training, evaluation, and ethical considerations.What You Will Learn:Key concepts and mathematical foundations of Generative AIArchitectural differences and applications of GANs, VAEs, autoregressive models, and diffusion modelsTransformer-based generative models, including GPT and DALL·EBest practices for model training, evaluation, and optimizationEthical implications and responsible AI practicesCourse Structure:1. Overview and Fundamentals of Generative AIDefinition and core concepts of generative models vs. discriminative modelsHistorical background and key milestones (e.g., Boltzmann Machines, VAEs, GANs)Applications: Text, image, audio, synthetic data, and moreKey advantages and challenges (e.g., creativity, bias, computational costs)2. Mathematical and Statistical UnderpinningsProbability distributions and latent variablesBayesian inference basics: Prior, likelihood, posteriorInformation theory concepts: Entropy, KL-Divergence, mutual information3. Core Generative Model FamiliesGANs: Generator-discriminator architecture, training challenges, variations (DCGAN, WGAN, StyleGAN)VAEs: Encoder-decoder architecture, ELBO objective, trade-offs with GANsAutoregressive Models: PixelCNN, PixelRNN, direct probability estimationNormalizing Flows: Invertible transformations, real-world applications4. Transformer-Based Generative ModelsSelf-attention mechanism, encoder-decoder vs. decoder-only modelsLLMs: GPT family (GPT-2, GPT-3, GPT-4) and training strategiesText-to-image models: DALL·E, Stable Diffusion, challenges and ethical issues5. Training Generative ModelsData collection and preprocessing for consistent inputOptimization and loss functions (adversarial loss, reconstruction loss)Hardware and software ecosystems (TensorFlow, PyTorch)Practical techniques: Hyperparameter tuning, gradient penalty, transfer learning6. Evaluation and MetricsQuantitative Metrics: Inception Score (IS), Fréchet Inception Distance (FID), perplexityQualitative Evaluation: Human perceptual tests, user studiesChallenges in measuring semantic correctness and creativity7. Ethical, Social, and Legal ImplicationsBias in training data and mitigation strategiesContent authenticity, deepfakes, and watermarkingCopyright issues and ownership of AI-generated contentResponsible deployment and transparency frameworks8. Advanced Topics and Latest ResearchDiffusion Models: Denoising diffusion models and applicationsMultimodal AI: Cross-modal retrieval and generationReinforcement Learning for Generative Models: Controlled generation strategiesSelf-Supervised Learning: Contrastive learning, masked autoencodingFuture Trends: Real-time 3D generation, foundation modelsThis course will give you a structured and in-depth understanding of Generative AI, equipping you with the knowledge and confidence to tackle real-world challenges and succeed in technical interviews.
