{"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":451,"pdf_page":467,"physical_index":467,"physical_index_base":0,"printed_page_status":"recognized","page_sha256":"0e1116038ac7de42107872c57d0c4cd3520f2c50fa8fe608e1fff9da03cc5af0"},"chapter":"第16章","content":"成本(cost)\n\n损失的同义词。\n\n交叉熵（cross-entropy）\n\n对数损失函数向多类别分类问题进行的一种泛化。交叉熵可以量化两种概率分布之间的差异。\n\n自定义Estimator(CustomEstimator)\n\n按照这些说明自行编写的Estimator,与预创建的Estimator相对。\n\nD\n\n数据集(data set)\n\n一组样本的集合。\n\nDataset API (tf.data)\n\n一种高级别的TensorFlowAPI，用于读取数据并将其转换为机器学习算法所需的格式。tf.data.Dataset对象表示一系列元素，其中每个元素都包含一个或多个张量。tf.data.Iterator对象可获取Dataset中的元素。\n\n决策边界(decision boundary)\n\n在二元分类或多类别分类问题中，模型学到的类别之间的分界线。例如，在如图D-4所示的某个二元分类问题的图片中，决策边界是橙色类别和蓝色类别之间的分界线。\n\n附录D 术语列表\n\n451","previous_page":450,"next_page":452}