人人可懂的数据科学

- 书名:人人可懂的数据科学
- 作者: JohnD.Kelleher JBrendanTierney
- 格式:EPUB,AZW3,MOBI
- 时间:2024-06-21
- 评分:
- ISBN:9787111637264
内容简介:
数据科学的主要目标就是通过数据分析来改进决策,它与数据挖掘、机器学习等领域紧密相关,但范围更广。《人人可懂的数据科学》简要介绍了该领域的发展、基础知识,并阐释了数据科学项目的各个阶段。书中既考虑数据基础架构和集成多个数据源数据所面临的挑战,又介绍机器学习基础并探讨如何应用机器学习专业技术解决现实问题。还综述了伦理和法律问题、数据法规的发展以及保护隐私的计算方法。最后探讨了数据科学的未来影响,并给出数据科学项目成功的原则。
约翰· D.凯莱赫(John D. Kelleher) 布伦丹·蒂尔尼(Brendan Tierney) 著:约翰· D.凯莱赫(John D. Kelleher) 是都柏林理工学院计算机科学学院的教授以及信息、通信和娱乐研究所的学术负责人。他的研究得到了ADAPT中心的支持,该中心由爱尔兰科学基金会(Grant 13 / RC / 2106)资助,同时也接受欧洲区域发展基金的资助。 他还是《Fundamentals of Machine Learning for Predictive Data Analytics》的作者之一。
布伦丹·蒂尔尼(Brendan Tierney)是都柏林理工学院计算机科学学院的讲师,同时也是Oracle ACE 主任,还著有多本基于Oracle技术的数据挖掘类著作。
下载地址:
标签:
文章链接:https://www.dushupai.com/book-content-20094.html(转载时请注明本文出处及文章链接)
- 上一篇: 少年读史记:辩士纵横天下
- 下一篇: 仁者无敌
最新评论:
更多
-
玥2022-07-09这个翻译太差了,和机翻没什么区别
-
晓时2023-06-01用来大致了解还是很不错的
-
……2020-06-27作为小白看完很有收获,译者大大辛苦啦
最新书摘:
更多
-
enoch20902019-02-12A neural network consists of a set of neurons that are con nected together. A neuron takes a set of numeric values asinput and maps them to a single output value. At its core, aneuron is simply a multi-input linear-regression functionThe only significant difference between the two is that ina neuron the output of the multi-input linear-regressionfunction is passed through another function that is calledthe activation function.
-
enoch20902019-02-12A frequent mistake that many inexperienced data scientists make is to focus their efforts on the modeling stageof the CRISP-DM and to rush through the other stagesThey may think that the really important deliverable froma project is the model, so the data scientist should devotemost of his time to building and finessing the model Hovever, data science veterans will spend more time on ensuring that the project has a clearly defined focus and that it has the right data. For a data science project to succeed, adata scientist needs to have a clear understanding of the business need that the project is trying to solve. So the
-
enoch20902019-02-12The distinctive feature of GPUS is that they cancarry out fast matrix multiplications. However, matrix multiplications are useful not only for graphics rendering but also for ML. In recent years, GPUS have been adapted and optimized for ML use, which has contributed to large speedups in data processing and model training. User-friendly data science tools have also become available and lowered the barriers to entry into data science. Taken to-gether, these developments mean that it has never beeneasier to collect, store, and process data.
猜你喜欢:
-
经济管理
-
经济管理
-
经济管理
-
经济管理
-
经济管理
-
经济管理
-
经济管理
-
经济管理