Information theory and inference, taught together in this exciting textbook, lie at the heart of many important areas of modern technology - communication, signal processing, data mining, machine learning, pattern recognition, computational neuroscience, bioinformatics and cryptography. The book introduces theory in tandem with applications. Information theory is taught alongside practical communication systems such as arithmetic coding for data compression and sparse-graph codes for error-correction. Inference techniques, including message-passing algorithms, Monte Carlo methods and variational approximations, are developed alongside applications to clustering, convolutional codes, independent component analysis, and neural networks. Uniquely, the book covers state-of-the-art error-correcting codes, including low-density-parity-check codes, turbo codes, and digital fountain codes - the twenty-first-century standards for satellite communications, disk drives, and data broadcast. Richly illustrated, filled with worked examples and over 400 exercises, some with detailed solutions, the book is ideal for self-learning, and for undergraduate or graduate courses. It also provides an unparalleled entry point for professionals in areas as diverse as computational biology, financial engineering and machine learning.
我感觉啊,这本书写的条理很清晰,可是太简明扼要了好像,我读不懂。可我碰到的问题上面有介绍,我又没找到其它地方,我只有读这个,可我读不懂。我郁闷。
评分学习信息论的时候,老师推荐的,然后就买来了。实例很多,习题也很经典,花费了一个学期看了一遍,感觉对信息论的理解完全高了好多个层次。
评分我感觉啊,这本书写的条理很清晰,可是太简明扼要了好像,我读不懂。可我碰到的问题上面有介绍,我又没找到其它地方,我只有读这个,可我读不懂。我郁闷。
评分1.刚从图书馆借到这本书,顺着书中的支持网站,发现作者把公开课视频也免费放到网上了,还可以直接下到英文原版电子版,这是什么精神~ ”A series of sixteen lectures covering the core of the book "Information Theory, Inference, and Learning Algorithms (Cambridge Un...
评分可惜看过了,理解不深刻,又忘了。 准备拾起来,虽然基本上工作用不上,就当是完成一个念想吧! 加油!
信息论和机器学习是一个硬币的两面。传统的信息论两条理论上的香农,工程应用是通信。具体的:贝叶斯数据模型,蒙特卡洛,变分法,聚类算法,神经网络。大脑是压缩和通信系统
评分好书好书太多了,还要继续读第三遍
评分机器学习领域中的 Feynman。
评分机器学习领域中的 Feynman。
评分教科书的榜样
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