He said literature and the arts are critical to what he called normalization after decades of military rule.

他表示文学和艺术对于经过几十年军事统治后他所化至关重要。

It's probably worth mentioning there's another set of parameters associated with those normalization steps that this explanation has skipped over, but like the bias vector, they account for a very trivial proportion of the total.

能值得提是， 还有另组与这些化步骤相关参数被这个解释忽略了，但就像偏置向量样， 它们在整个参数总量中所占比例非常微小。

You want to have some basic programming skills, but then you're going to dive into key concepts like reward modeling, supervised fine-tuning, mixture of experts layers, RMS, norm, rope, KV caching and more.

需掌握些基础编程技能，随后深入研究诸如奖励建模、监督微调、专家层混合、均方根化、旋转位置编码、KV缓存等关键概念。

Then we find the squared distance from that expected value, and we, I guess you could call it, normalize it by the expected value.

然后我们计算与该期望值平方距离， 并将其，我猜称之为， 通过期望值进行化处理。

And so what's the normalization constant that I need to normalize it? 1.

那么，我需要化常数是什么呢？1.

And so sometimes we write them real neatly with the normalizing constants and place the mean and the variance in places that you can recognize very easily.

因此，有时我们会用化常数将它们整齐地写出来，并将平均值和方差放在您很容易识别地方。

So this conjugacy thing is great if you know about the distribution because it won't require you to keep track of any of your normalizing constants or solve that integral in the denominator at the end of the day.

所如果了解分布，这个共轭就很好了，因为它不需要跟踪任何化常数，也不需要在天结束时解决分母中积分。

And a lot of your priors on non-compact spaces, unbounded things like mu, turn out that you want to give a prior that can't be normalized quite often.

而在非紧空间上很多先验，比如 mu 这样无界事物， 结果证明想给出个不能经常化先验。

So e to the minus 1 half x bar minus mu squared over sigma squared over n. And I've gotten rid of my normalizing constants right here.

所 e 到负 1 半 x bar 减去 mu 平方超过 sigma 平方超过 n。我在这里删除了我化常量。

So I'll say pi p would be proportional to, so there's a normalizing constant in here, p to the alpha minus 1, 1 minus p, beta minus 1.

所我会说 pi p 与 成正比，所这里有个化常数，p 到 alpha 减 1,1 减去 p， beta 减去 1。