[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"$fFIn4M7Y288kNhYTSp-jlhp5W-IVzjtPEy-nURRw9AmQ":3},{"answer":4,"createTime":5,"id":6,"options":7,"origin":12,"question":16,"related":17,"source":27,"type":28},[],"2026-01-06 16:56:36",304679554,[8,9,10,11],"仅深度学习需要数据训练","深度学习通过深层网络自动提取特征","机器学习无法处理复杂任务","深度学习不依赖算法",{"courseId":13,"courseImg":14,"courseName":15},"53e1d2ef4961cca8eea3e23969ad2cb9","https:\u002F\u002Ftihai-oss-cloud.itihey.com\u002Fimg\u002F03a579384a6dc297c89809b582fcc767.png","默认课程","深度学习与机器学习的本质区别在于?( )",[18,29,38,47,56,65,74,81,90,99],{"answer":19,"createTime":5,"id":20,"options":21,"question":26,"source":27,"type":28},[],304679545,[22,23,24,25],"1943 年 M-P 模型提出","2012 年 AlexNet 在 ImageNet 挑战赛夺冠","1986 年 BP 算法诞生","2006 年深度信念网络提出","标志着深度学习正式登上历史舞台的事件是?( )","v1",0,{"answer":30,"createTime":5,"id":31,"options":32,"question":37,"source":27,"type":28},[],304679546,[33,34,35,36],"无需人工设计特征,实现端到端自动特征学习","依赖浅层模型架构处理复杂数据","通过深层网络结构突破维度灾难瓶颈","在图像识别等任务中达到超越人类的性能","与传统机器学习相比,深度学习的核心优势不包括?( )",{"answer":39,"createTime":5,"id":40,"options":41,"question":46,"source":27,"type":28},[],304679547,[42,43,44,45],"无法处理线性分类问题","缺乏自主学习功能","只能处理图像数据","参数量过大","M-P 模型作为深度学习的早期基础,其主要缺陷是?( )",{"answer":48,"createTime":5,"id":49,"options":50,"question":55,"source":27,"type":28},[],304679548,[51,52,53,54],"完全基于注意力机制,摒弃循环和卷积结构","采用多层感知器实现特征提取","引入池化层降低维度","依赖时序循环连接建模","Transformer 架构的核心创新是?( )",{"answer":57,"createTime":5,"id":58,"options":59,"question":64,"source":27,"type":28},[],304679549,[60,61,62,63],"无法处理图像数据","虚假信息生成与传播","参数量过小","只能运行在本地设备","生成式人工智能面临的首要风险是?( )",{"answer":66,"createTime":5,"id":67,"options":68,"question":73,"source":27,"type":28},[],304679550,[69,70,71,72],"记忆遗忘","选择性关注","逻辑推理","语言生成","注意力机制的工作原理类似于人类的?( )",{"answer":75,"createTime":5,"id":76,"options":77,"question":80,"source":27,"type":28},[],304679551,[25,24,78,79],"1998 年 LeNet-5 提出","2012 年 AlexNet 夺冠","深度学习第三次热潮的起点是?( )",{"answer":82,"createTime":5,"id":83,"options":84,"question":89,"source":27,"type":28},[],304679552,[85,86,87,88],"图像分类","时序数据处理(语音、文本)","特征降维","强化学习","循环神经网络(RNN)的核心应用场景是?( )",{"answer":91,"createTime":5,"id":92,"options":93,"question":98,"source":27,"type":28},[],304679553,[94,95,96,97],"技术迭代与市场需求的矛盾","发展创新与风险管控的平衡","硬件成本与算法精度的矛盾","数据规模与存储能力的矛盾","人工智能伦理治理的核心矛盾是?( )",{"answer":100,"createTime":5,"id":6,"options":101,"question":16,"source":27,"type":28},[],[8,9,10,11]]