{"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":194,"pdf_page":210,"physical_index":210,"physical_index_base":0,"printed_page_status":"recognized","page_sha256":"7b6d59e8a5f8ebe39910355f310029b0cf5edb51f92b93db0d3948d8cfaeee34"},"chapter":"第7章","content":"7.1.1 深度学习为何崛起\n\n深度学习并不是新兴理论，早在几十年前，深度学习和神经网络的基本理念就已经比较完备了。关于深度学习之所以现在才开始流行的原因，我们先来看图7-2。\n\n模型能达到好的效果，背后是数据、计算力和算法在支持。深度学习之所以最近火起来，数据量的增加、计算力的提升和算法的进步都为之做出了贡献。但从图7-2可以看出，数据量的增加是最主要的因素。模型的表现力随着数据量的增加而提升，这是毋庸置疑的。但在数据量比较少的时候，神经网络模型相比传统的学习算法没有明显的优势，只有当训练集很大时，神经网络的效果才会比传统算法更好。所以，随着近些年数据接触量爆炸式增加，深度学习的优势越来越明显。\n\n7.1.2 从逻辑回归到浅层神经网络\n\n我们知道，深度学习主要指多层神经网络，让我们先来了解一下什么是神经网络。神经网络并没有听起来那么高深，其本质上也属于机器学习的一种模型，就像前几章介绍的常见模型一样。\n\n还记得逻辑回归模型吗？这是我们在第6章介绍过的一种分类器。逻辑回归模型将输入变量按一定权重线性组合求和，然后对得到的值施加一个名为sigmoid的函数变换，即 $\\ g ( z ) =$\n\n人工智能与大数据技术导论\n\n194","previous_page":null,"next_page":195}