[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"$fOSSL_90ze1peK5kwNm12qV_3ztJmpeNpPgdkreJHOjk":3},{"answer":4,"createTime":5,"id":6,"options":7,"origin":12,"question":16,"related":17,"source":28,"type":41},[],"2025-04-28 08:26:25",1069534832,[8,9,10,11],"卷积层","池化层","全连接层","归一化层",{"courseId":13,"workId":14,"workName":15},"1000128449","61792898","第八章单元测试","在卷积神经网络(CNN)中,以下哪个层主要负责减少数据维度?( )",[18,30,38,42,51,56],{"answer":19,"createTime":5,"id":20,"options":21,"question":27,"source":28,"type":29},[],1069534816,[22,23,24,25,26],"卷积核的大小","步长","卷积核的数量","激活函数的类型","学习率","在卷积神经网络中,卷积运算的参数设置对模型性能有重要影响.以下哪些选项是卷积运算中需要考虑的参数?( )","v2",1,{"answer":31,"createTime":5,"id":32,"options":33,"question":36,"source":28,"type":37},[],1069534818,[34,35],"对","错","在深度学习中,池化操作可以有效地减少特征图的尺寸,从而降低计算复杂度和防止过拟合.最大池化和平均池化是两种常见的池化方法.根据池化的定义,最大池化总是会导致特征图尺寸的减小,而平均池化则不一定会缩小特征图的尺寸.( )",3,{"answer":39,"createTime":5,"id":6,"options":40,"question":16,"source":28,"type":41},[],[8,9,10,11],0,{"answer":43,"createTime":5,"id":44,"options":45,"question":50,"source":28,"type":41},[],1069534835,[46,47,48,49],"加法","乘法","积分或加权和","求导","卷积运算的定义涉及将两个函数合并成一个新的函数.这一过程通常包括哪种操作?( )",{"answer":52,"createTime":5,"id":53,"options":54,"question":55,"source":28,"type":37},[],1069534839,[34,35],"多隐含层的多层感知器(MLP)是一种深度学习模型,主要用于处理复杂的非线性问题.由于其结构的复杂性,MLP在图像识别和自然语言处理等领域表现出色,因此可以认为多隐含层MLP只能用于这两种应用领域.( )",{"answer":57,"createTime":5,"id":58,"options":59,"question":64,"source":28,"type":41},[],1069534840,[60,61,62,63],"TensorFlow更适合于工业级应用,支持大规模分布式计算","PyTorch在动态计算图方面更具优势,仅适合于研究和快速原型开发","TensorFlow仅限于深度学习,而PyTorch可以用于多种机器学习任务","PyTorch的社区支持和文档资源比TensorFlow更丰富","在机器学习框架中,TensorFlow和PyTorch各自具有不同的特点和应用场景.以下关于TensorFlow与PyTorch的描述,哪一项是正确的?( )"]