{"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":212,"pdf_page":228,"physical_index":228,"physical_index_base":0,"printed_page_status":"recognized","page_sha256":"cd1d377feb32fc48c34597d5d99f52c8cae10cc3626fccbdb2621ee0a3748914"},"chapter":"第7章","content":"经网络的配置变得更加简单，而不必考虑所在的框架。谷歌的TensorFlow目前支持Keras作为后端，而微软的CNTK也将在短时间内获得支持。\n\nKeras的优点是对用户友好，易于上手，高度拓展，可以在CPU或GPU上无缝运行，完美兼容Theano和TensorFlow；缺点是不能有效地作为一个独立的框架使用。\n\nCNTK\n\n微软的CNTK（计算网络工具包）是一个用来增强模块化和保持计算网络分离的库，提供学习算法和模型描述。在需要大量服务器进行计算的情况下，CNTK可以同时利用多台服务器。据说CNTK在功能上接近谷歌的TensorFlow，但速度比对方要快一些。\n\nCNTK的优点是高度灵活，允许分布式训练，支持C++、C#、Java和Python；缺点是由一种新的语言—NDL（网络描述语言）实现，缺乏可视化。\n\n人工智能与大数据技术导论\n\n212","previous_page":211,"next_page":null}