{"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":214,"pdf_page":230,"physical_index":230,"physical_index_base":0,"printed_page_status":"recognized","page_sha256":"f392b74a040bb675710c0ab7e2cfaaa03bf6c64c1370371700945b78a2eff39d"},"chapter":"第8章","content":"应该尽可能使用最高层级的API。较高级别的API更易于使用，虽然不如底层API那么灵活。建议先从最高级API入手，让所有组件正常运作起来。如果你希望在某些特定的建模方面更加灵活一些，则可以选择下一个级别。每个级别都是使用低级API构建的，因此降低层级是没问题的。\n\nTensorFlow由以下两个组件组成：\n\n图协议（graph protocol）缓冲区。\n\n●执行（分布式）图的运行环境（runtime）。\n\n这两个组件类似于Python代码和Python编译器。正如在多个平台上实现了Python编译器，从而可以执行Python代码，TensorFlow可以在不同的硬件平台（TPU、CPU和GPU）上运行图。\n\n8.1.1 tf.estimator API\n\ntf.estimator是最常用的API。我们可以使用它来完成机器学习中的大部分任务。当然，我们也可以使用较低层级（原生）的TensorFlowAPI，但使用tf.estimator会大大减少代码量。tf.estimator与scikit-learnAPI兼容。正如前面提到的，scikit-learn是热门的开源机器学习库，它是基于Python的。目前全球有超过10万名用户在使用scikit-learn。以下是用tf.estimator实现的线性分类程序（伪代码）：\n\n8.1.2 Pandas 速成\n\nPandas是Python的一个开源数据分析的库，它广泛用在TensorFlow代码中。Pandas提供的数据结构Series和DataFrame极大地简化了数据分析过程中的一些烦琐操作。Series是一维数组，比如我们先引入Pandas API库，然后创建一个Series对象：\n\nSeries就是一个一维数组，每个数据对应一个索引值。上述例子的索引值和数据就是：\n\n人工智能与大数据技术导论\n\n214","previous_page":null,"next_page":215}