……内容很棒，但印刷质量差（对比度很低等）。这不是关于电子书与纸质书、或 PDF 与其他格式的争论。这是关于印刷书籍的质量。我花了 $$$ 从 MIT Press 买了 Kevin Murphy 的《Probabilistic Machine Learning》。纸张质量极佳，印刷精美。值得收藏。多年后我还会读它。就像我读（并拥有）几十年前的书一样。我可能会把它们传给某个值得的人，……
…Great content, but poor printing (very low contrast, etc). This is not a debate on ebooks vs paper books, or PDF vs something else. It's about quality of printed books. I paid $$$ for Kevin Murphy's _Probabilistic Machine Learning_ from MIT Press. Excellent quality paper, great printing. It's a keeper. I'll be reading it years from today. Just as I read (and have) books from decades ago. I might pass 'em on to somebody well deserving,…
这大概是我见过的最好的、毫无废话的应用数学“教程”了——就是 Kevin P. Murphy 写的《Probabilistic Machine Learning: An Introduction》里的数学背景附录。
Probably the best applied math "tutorial" without all the bullshit I've yet seen is the mathematical background appendix in Probabilistic Machine Learning: An Introduction by Kevin P. Murphy.
我是 Murphy 那本 ML 书[1]的铁杆粉丝。[1] https://smile.amazon.com/Probabilistic-Machine-Learning-Intr... 它几乎涵盖了你开始做 ML 研究所需的所有数学知识。我觉得对于搞 ML 的 CS 人来说，这就是最理想的“一书统揽天下”之作。不过，搞 ML 的纯数毕业生可能会觉得……
I am a big fan of Murphy's ML book [1]. [1] https://smile.amazon.com/Probabilistic-Machine-Learning-Intr... It covers almost all the math you'd need to start doing ML research. I find it to be the ideal 'one book to rule them all' book for CS people in ML. Although, pure math grads in ML might find…
……技术可能可以卓有成效地应用，特别是如果你有一些数学/计算机科学博士所没有的领域知识。对于 OP - 我处于类似的情况，一直在读 Kevin Murphy 的《Probabilistic Machine Learning》，它相当庞大且密集，但也非常清晰。
…techniques can probably be fruitfully applied, especially if you have some domain knowledge that the math/CS PhDs don’t have. For OP - I’m in a similar situation and have been going through Kevin Murphy’s “Probabilistic Machine Learning”, which is pretty massive and dense but also very lucid.
机器学习是个庞大的领域，如果你想从头开始解释/探索，那么 Kevin Murphy 的《Probabilistic Machine Learning: An Introduction》非常好，但有点大部头。如果你只想专注于神经网络，我发现 Michael Nielsen 的《Neural Networks and Deep Learning》很不错，可以在 http://neuralnetworksanddeeplearning.com/ 获取，……
Machine learning is an enormous field, and if you are after a explanation/exploration from the ground up, then Kevin Murphy's Probabilistic Machine Learning: An Introduction is very good, but a bit of a tome. If instead, you want to focus on neural networks, I found Michael Nielsen's Neural Networks and Deep Learning, available at http://neuralnetworksanddeeplearning.com/,…
…https://www.brendangregg.com/bpf-performance-tools-book.html Furthermore, nothing keeps you from reading books about topics peripheral to computer science, say to keep up with the general vibes: - https://www.amazon.com/Probabilistic-Machine-Learning-Introd... - https://www.amazon.com/Deep-Learning-Foundations-Christopher... - https://www.amazon.com/Joy-Abstraction-Exploration-Category-... I find that all of these contribute significantly to my…
……内容很棒，但印刷质量差（对比度很低等）。这不是关于电子书与纸质书、或 PDF 与其他格式的争论。这是关于印刷书籍的质量。我花了 $$$ 从 MIT Press 买了 Kevin Murphy 的《Probabilistic Machine Learning》。纸张质量极佳，印刷精美。值得收藏。多年后我还会读它。就像我读（并拥有）几十年前的书一样。我可能会把它们传给某个值得的人，……
…Great content, but poor printing (very low contrast, etc). This is not a debate on ebooks vs paper books, or PDF vs something else. It's about quality of printed books. I paid $$$ for Kevin Murphy's _Probabilistic Machine Learning_ from MIT Press. Excellent quality paper, great printing. It's a keeper. I'll be reading it years from today. Just as I read (and have) books from decades ago. I might pass 'em on to somebody well deserving,…
这大概是我见过的最好的、毫无废话的应用数学“教程”了——就是 Kevin P. Murphy 写的《Probabilistic Machine Learning: An Introduction》里的数学背景附录。
Probably the best applied math "tutorial" without all the bullshit I've yet seen is the mathematical background appendix in Probabilistic Machine Learning: An Introduction by Kevin P. Murphy.
我是 Murphy 那本 ML 书[1]的铁杆粉丝。[1] https://smile.amazon.com/Probabilistic-Machine-Learning-Intr... 它几乎涵盖了你开始做 ML 研究所需的所有数学知识。我觉得对于搞 ML 的 CS 人来说，这就是最理想的“一书统揽天下”之作。不过，搞 ML 的纯数毕业生可能会觉得……
I am a big fan of Murphy's ML book [1]. [1] https://smile.amazon.com/Probabilistic-Machine-Learning-Intr... It covers almost all the math you'd need to start doing ML research. I find it to be the ideal 'one book to rule them all' book for CS people in ML. Although, pure math grads in ML might find…
……技术可能可以卓有成效地应用，特别是如果你有一些数学/计算机科学博士所没有的领域知识。对于 OP - 我处于类似的情况，一直在读 Kevin Murphy 的《Probabilistic Machine Learning》，它相当庞大且密集，但也非常清晰。
…techniques can probably be fruitfully applied, especially if you have some domain knowledge that the math/CS PhDs don’t have. For OP - I’m in a similar situation and have been going through Kevin Murphy’s “Probabilistic Machine Learning”, which is pretty massive and dense but also very lucid.
机器学习是个庞大的领域，如果你想从头开始解释/探索，那么 Kevin Murphy 的《Probabilistic Machine Learning: An Introduction》非常好，但有点大部头。如果你只想专注于神经网络，我发现 Michael Nielsen 的《Neural Networks and Deep Learning》很不错，可以在 http://neuralnetworksanddeeplearning.com/ 获取，……
Machine learning is an enormous field, and if you are after a explanation/exploration from the ground up, then Kevin Murphy's Probabilistic Machine Learning: An Introduction is very good, but a bit of a tome. If instead, you want to focus on neural networks, I found Michael Nielsen's Neural Networks and Deep Learning, available at http://neuralnetworksanddeeplearning.com/,…
……https://www.brendangregg.com/bpf-performance-tools-book.html 此外，完全没人能阻止你去读一些计算机科学周边领域的书，比如顺便跟上大众的 vibe：- https://www.amazon.com/Probabilistic-Machine-Learning-Introd... - https://www.amazon.com/Deep-Learning-Foundations-Christopher... - https://www.amazon.com/Joy-Abstraction-Exploration-Category-... 我觉得这些对我都有很大帮助……
…https://www.brendangregg.com/bpf-performance-tools-book.html Furthermore, nothing keeps you from reading books about topics peripheral to computer science, say to keep up with the general vibes: - https://www.amazon.com/Probabilistic-Machine-Learning-Introd... - https://www.amazon.com/Deep-Learning-Foundations-Christopher... - https://www.amazon.com/Joy-Abstraction-Exploration-Category-... I find that all of these contribute significantly to my…