{"resolved":true,"match_type":"uuid","source_type":"original_book","book_id":"cd4f7678-cd9b-4971-bde0-e9ad46a6cc71","title":"人工智能与大数据技术导论","author":null,"edition":null,"publisher":null,"year":null,"isbn":"9787302517986","requested_by":"printed_page","book":{"source_type":"original_book","book_id":"cd4f7678-cd9b-4971-bde0-e9ad46a6cc71","id":"cd4f7678-cd9b-4971-bde0-e9ad46a6cc71","uuid":"cd4f7678-cd9b-4971-bde0-e9ad46a6cc71","title":"人工智能与大数据技术导论","normalized_title":"人工智能与大数据技术导论","author":null,"edition":null,"publisher":null,"year":null,"isbn":"9787302517986","isbns":["9787302517986"],"parsed":true,"ai_readable":true,"document_type":"book","page_count":490,"navigation_url":"https://workshop.closeclaw.top/ai/book/cd4f7678-cd9b-4971-bde0-e9ad46a6cc71","title_url":"https://workshop.closeclaw.top/ai/title/%E4%BA%BA%E5%B7%A5%E6%99%BA%E8%83%BD%E4%B8%8E%E5%A4%A7%E6%95%B0%E6%8D%AE%E6%8A%80%E6%9C%AF%E5%AF%BC%E8%AE%BA","fixed_page_url_template":"https://workshop.closeclaw.top/ai/book/cd4f7678-cd9b-4971-bde0-e9ad46a6cc71/page/{printed_page}","path_api_url_template":"https://workshop.closeclaw.top/api/ai/book/cd4f7678-cd9b-4971-bde0-e9ad46a6cc71/page/{printed_page}","read_url":"https://workshop.closeclaw.top/api/ai/read/cd4f7678-cd9b-4971-bde0-e9ad46a6cc71","page_url":"https://workshop.closeclaw.top/api/ai/books/cd4f7678-cd9b-4971-bde0-e9ad46a6cc71/page","search_url":"https://workshop.closeclaw.top/api/ai/books/cd4f7678-cd9b-4971-bde0-e9ad46a6cc71/search"},"page":{"printed_page":461,"pdf_page":477,"physical_index":477,"physical_index_base":0,"printed_page_status":"recognized","page_sha256":"2a2df19ec2585b8b9ec98126bdc1242167629bdfab702fab9245935f5fc3e749"},"chapter":"第16章","content":"模型训练(model training）\n\n确定最佳模型的过程。\n\n动量（Momentum）\n\n一种先进的梯度下降法，其中学习步长不仅取决于当前步长的导数，还取决于之前一步或多步的步长的导数。动量涉及计算梯度随时间而变化的指数级加权移动平均值，与物理学中的动量类似。动量有时可以防止学习过程被卡在局部最小的情况。\n\n多类别分类(multi-classclassification)\n\n区分两种以上类别的分类问题。例如，枫树大约有128种，因此，确定枫树种类的模型就属于多类别模型。反之，仅将电子邮件分为两类（“垃圾邮件”和“非垃圾邮件”）的模型属于二元分类模型。\n\n多项分类(multinomialclassification)\n\n多类别分类的同义词。\n\nN\n\nNaN陷阱(NaN trap)\n\n模型中的一个数字在训练期间变成NaN，这会导致模型中的很多或所有其他数字最终也会变成NaN。NaN是“非数字”的缩写。\n\n负类别(negative class)\n\n在二元分类中，一种类别称为正类别，另一种类别称为负类别。正类别是我们要寻找的类别，负类别则是另一种可能性。例如，在医学检查中，负类别可以是“非肿瘤”。在电子邮件分类器中，负类别可以是“非垃圾邮件”。另可参阅正类别。\n\n神经网络(neuralnetwork)\n\n一种模型，灵感来源于脑部结构，由多个层构成（至少有一个是隐藏层），每个层都包含简单相连的单元或神经元（具有非线性关系）。\n\n神经元(neuron)\n\n神经网络中的节点，通常用于接收多个输入值并生成一个输出值。神经元通过将激活函数（非线性转换）应用于输入值的加权和来计算输出值。\n\n节点(node)\n\n多含义术语，可以理解为下列两种含义之一：\n\n●隐藏层中的神经元。\n\n附录D 术语列表\n\n461","previous_page":460,"next_page":462}