@pepsin 抱歉，你看错了，里面有我。

@pepsin https://nips.cc/Conferences/2015/Schedule

@dcoder 我深刻地理解，如果无法融入一个社区，并不是社区的错，而是我个人的问题。那么只有三种选项 1 ）闭嘴 2 ）改变环境 3 ）去一个新环境。
我自认为做不到 2 ）。所以我选择 1 ） or 3 ）了。

@dcoder 之前不在家，电脑上没有中文 IME 。你这句话说的挺反智的。說英文和提出概念，并不能证明一个人是在自 high 。何况我提出的这些概念，都是有明确目的的。比如上面所说的 SVM 的例子，就是在为 wizardforcel 同学阐明 ML 和 optimization 的关系。

我发现我确实不适合在 V2EX 跟大家交流，我之前在这里推荐的非常好的书籍，并没有几个人关注。反而这种我一时好奇提出的问题，让很多半懂不懂的人都能来参与几句傲慢地鄙视一下别人，引起了这么大的关注。这个问题，的确没有什么意义，我也几乎没有从回复中收获到新的认识。如果可行的话，我是希望管理员能够删除这个帖子的。

谢谢各位，祝好。

@wizardforcel @wizardforcel How can you be so confident that ML - DL is not bleeding edge research given you only know /something/ about clustering/classifying? SVM is also a large concept. I do not think a "培训班" will tell you anything about the representation theorem in feature space. I am also wondering whether you know anything about reinforcement learning, which is another main topic in ML.

I do admit that some elementary technique in ML is understood by some ordinary programmers, as we all know how to compute +-*/ in math. "ML - DL is not bleeding edge research" is something like "Math - Algebraic geometry is not bleeding edge research" :-)

The reason that I mention optimization frequently is that nearly all ML problems are essentially optimization problems. For example, given that you have already known SVM, I'll use SVM as an example :) The soft margin SVM or equivalently, the slack variable SVM, is the same as the optimization problem of hinge loss regularized by L_2 norm regularization term. The state of art method to solving SVM is using the popular optimization algorithm --- stochastic coordinate descent.

BTW, do not think too highly of yourself if you learn something about ML in one year :-) Within 6 months of learning ML, I had published spotlight research paper in NIPS. I'm not one of the best in the field now and we both need to learn new stuff to become better = D

@pepsin Yes, you can definitely say my statements are full of jargon, as it will be if you communicate with any professional, if you do not understand them, but this judgement does not have any credit. Welcome any solid comments, given that the commenter really understands what is happening.

@wizardforcel Actually some of them are yet to be discovered. Tons of algorithms in ML (not DL) do not have theoretical guarantee for their asynchronous parallel versions. This is a rising topic in ML/Optimization now. If you can give a good bound, you can publish a paper on top conference NIPS/ICML etc.

@wizardforcel You are definitely kidding. DL is just a method of fitting. ML - DL includes but not limited to HMM, PCA, manifold learning and lots of optimization algorithms. They may have intersection, but they are definitely not DL. Do you know RKHS or L-BFGS? Do you know what will change if a single optimization variable is applied on different problems (nonconvex, strongly convex, general convex, nonsmooth)? Do you think a ordinary programmer understands them correctly?

@wizardforcel My network sucks, sorry for replying multiple times.

@wizardforcel I will not look into these elementary textbooks given I have much better understanding in these fields. I am simply against your claim "请注意 dl 算前沿科学，但是 ml 不算，图形更不算。", which is not even wrong.

@wizardforcel Given I already have much better understanding in those fields, I will not look into these elementary textbooks. I am simply against your claim "请注意 dl 算前沿科学，但是 ml 不算，图形更不算。", which is not even wrong.

@wizardforcel I won't look into those elementary books since I have much better understanding in these fields. I am simply against your claim that "请注意 dl 算前沿科学，但是 ml 不算，图形更不算。", which is not even wrong.

@wizardforcel Nearly every domain can incorprate some ml in their research. As a PhD in machine learning and optimization, I have no clue of what a "lower-order" algorithm means in your statement.

@doyel 2333 多沉？

@wizardforcel ml includes dl, period. dm is another topic.

@jsyangwenjie 我也没说只会调参能写 leaf 啊。。。一开始不是说调参不算写程序么，跑远了。

@jsyangwenjie 写个分布式扩展也许不用知道调参之类的。然而写个 leaf 就需要了：）
我觉得 computing related 的经验都算工程经验，因为没有这些，有些东西就是写不出来啊：）
而且说是调参，也不是一天到晚调参啊，就像写程序总不能一天到晚 hello world 吧 ww

夏天，妹子穿水手服就好了

@jsyangwenjie what do you mean by 工程经验。。听着像只有 computer system architecture 才算工程经验。。。