AI 与机器学习camdavidsonpilon.github.io编辑精选
面向黑客的概率编程与贝叶斯方法
Probabilistic Programming and Bayesian Methods for Hackers (2013)
2013 — 2018，它一次次回到首页
黑点是上首页（≥ 50 分）的那次投稿，空心点是没冲上去的投稿，点越大分数越高。
导读
《黑客的贝叶斯方法与概率编程》是一本开源的贝叶斯统计入门书，以 Python/PyMC 代码和交互式 Jupyter Notebook 为主线，强调“先实践、后数学”的学习路径，让读者通过动手改代码来理解概率编程。全书在 GitHub 上免费开放，也有纸质完整版。
为什么经久不衰：它把抽象的贝叶斯推断变成可以直接运行和修改的代码，多年来反复被推荐为最平易近人的概率编程入门，今天依然适合想用实践方式入门贝叶斯方法的程序员。
HN 评论精选
质疑“先理解后数学”的提法，认为数学本身就是理解，去掉数学会削弱理解。
这本书看起来很棒，我一定会读。不过书中“以计算/理解为先、数学为后”的说法让我不太舒服——数学本身就是理解。去掉数学其实也去掉了一部分理解，剩下的只是“遇到 x 就用模型 a，否则用模型 b”的流程，一旦碰到流程没覆盖的问题就会失效。更准确的说法应该是“先实用、后理论”。
fwdpropaganda查看英文原文
This looks amazing and I will definitely read it. As an aside, does this line bother anyone else? > programming from a computation/understanding-first, mathematics-second point of view Mathematics is understanding. What's implicit? The suggestion that mathematics isn't understanding, it's just funny symbols that no one understands. > The latter path is much more useful, as it denies the necessity of mathematical intervention at each step, that is, we remove often-intractable mathematical analysis as a prerequisite to Bayesian inference. In fact, I would say that by removing maths you're removing at least some amount of understanding. You're left with a workflow of "if x use model a else use model b." That will work until it doesn't, i.e. until you have to face a problem that isn't predicted by the workflow. Not saying that's a bad approach (it's certainly the most pragmatical) but it shows that the phrase "understanding first maths second" is misguided at best and disengenious at worst. I think a more appropriate phrase for what the author describes in the prologue would be "practicalities first, theory second".
推荐 MacKay 信息论课程和 Koller 概率图模型课程作为补充学习资源。
我推荐 David MacKay 的信息论入门课（YouTube 上有视频，配套书免费）。Daphne Koller 在 Coursera 上的三门概率图模型课程也非常好。
swframe2查看英文原文
I recommend David MacKay's Intro to Info Theory class: https://www.youtube.com/watch?v=BCiZc0n6COY There is a free book for it at: http://www.inference.org.uk/mackay/itila/ Daphne Koller's 3 coursara classes on Probabilistic Graphical Models are also really good.
肯定本书价值，但更推荐 Kruschke 的书及其“直觉→数学→代码”的讲解顺序。
这本书很有帮助，我做完了所有交互式示例。不过我更喜欢 Kruschke 的《Doing Bayesian Data Analysis》，它极其清晰：先用直觉引入概念，再讲数学，最后给代码，这个顺序非常有用。
glial查看英文原文
This book is helpful - I went through it and did all the interactive examples. Having said that, I much prefer "Doing Bayesian Data Analysis" by Kruschke. It is extremely clear. It introduces concepts with intuition first, then math, then code, which I find to be an extremely useful order.
称赞 notebook 形式让动手实践和参与改进都极其方便。
我一个月前通读了这本书，非常实用易懂。IPython notebook 格式让读者无需任何配置就能直接玩代码，放在 GitHub 上也让我能边读边方便地修正和澄清问题。
3JPLW查看英文原文
I worked my way through the book a month ago. It's very practical and understandable. The ipython notebook format makes it extremely easy to play with the code without worrying about any setup at all. And having it on github made it extremely easy to fix and clarify things as I went.
历次讨论
同类还值得读
如何写一个拼写纠正器
How to Write a Spelling Corrector
Peter Norvig 用一个几十行的 Python 程序演示如何做拼写纠正：先用编辑距离（删除、插入、替换、换位）生成所有可能的候选词，再用从大量文本中统计出的词频概率，选出…
AI 与机器学习上首页 10 次 · 2007–2022norvig.com编辑精选神经网络黑客指南
Hacker's Guide to Neural Networks
Andrej Karpathy 写的神经网络入门教程，面向程序员，从最简单的表达式求导讲起，逐步推导出反向传播和梯度下降，最后引出神经网络。
AI 与机器学习上首页 4 次 · 2014–2019karpathy.github.io编辑精选神经网络能逼近任意函数的可视化证明
A visual proof that neural nets can approximate any function
这一章来自 Michael Nielsen 的在线书《Neural Networks and Deep Learning》，用 sigmoid 单元叠加成局部隆起/平滑阶跃的图示…
AI 与机器学习上首页 4 次 · 2014–2022neuralnetworksanddeeplearning.com编辑精选论乔姆斯基与统计学习的两种文化
On Chomsky and the Two Cultures of Statistical Learning
Norvig 回应 Chomsky 对统计语言模型批评的文章，围绕“统计学习能否解释语言”展开，比较生成与解释、少参数模型与 Shannon 式高参数统计模型。
AI 与机器学习上首页 4 次 · 2011–2025norvig.com编辑精选语音与语言处理（第三版草稿，2017）
Speech and Language Processing, 3rd ed. draft (2017)
Jurafsky 与 Martin 经典教材《Speech and Language Processing》第三版草稿的官方页面，按章提供 PDF，覆盖语音识别、自然语言处理与语…
AI 与机器学习上首页 4 次 · 2018–2025web.stanford.edu编辑精选