Bitwarden 是一个开源的密码管理器，可以帮助你存储和保护你的密码。它有一个云服务，但是你也可以选择自己搭建一个私有的 Bitwarden 服务器（ 通过 docker 构建非常简单），这样你就可以完全控制你的数据。
本文介绍的是一个社区维护的 Bitwarden 服务器，叫做 Vaultwarden。Vaultwarden 兼容 Bitwarden 的客户端，支持 Bitwarden 的所有常用功能，并且官方需要订阅的高级功能都是免费的。
Abstract: The dominant sequence transduction models are based on complex recurrent or convolutional neural networks that include an encoder and a decoder. The best performing models also connect the encoder and decoder through an attention mechanism. We propose a new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two machine translation tasks show these models to be superior in quality while being more parallelizable and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014 English-to-German translation task, improving over the existing best results, including ensembles, by over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new single-model state-of-the-art BLEU score of 41.0 after training for 3.5 days on eight GPUs, a small fraction of the training costs of the best models from the literature.
前面说到, 换到 RouterOS 确实系统启动快了非常多, 资源消耗也非常小, 但是又引出了几个新的痛点…
此外感觉网络拓扑整的太复杂并没有什么用, 还是应该用奥卡姆剃刀剃一下, 这样出问题也好排查.
看了半天还是决定用主路由 OPNsense + 旁路由 OpenClash 分流. 主路由经过试用, OPNsense 似乎有更好的响应速度和易用性, 所以就用它踢掉 pfSense. OPNsense 有全面的 API 接口 , 方便做一些自动化的开发, 也不需要学 ROS 的语法.
而且, OPNsense有非常好看的前端界面!
因为实在受不了 pfSense 开机太慢而且 WebUI 巨卡 [1] , 和一些 DNS 设置奇奇怪怪的问题, 终于下定决心把主路由给换了. 调研使用了一波以后, 发现还是 RouterOS 可以比较相对简单地实现需求 (well, ROS 以复杂出名.. but [2] ), 那正好开坑写一个系列, 重组内网服务器.
Stanford University, Machine Learning, Andrew Ng, Coursera
Week 9: Anomaly Detection, Recommender Systems Part 2
| Movie | Alice (1) | Bob (2) | Carol (3) | Dave (4) | $x_1$ | $x_2$ |
|---|---|---|---|---|---|---|
| $x^{\left(1\right)}$ | 5 | 5 | 0 | 0 | 0.9 | 0 |
| $x^{\left(2\right)}$ | 5 | ? | ? | 0 | 1.0 | 0.01 |
| $x^{\left(3\right)}$ | ? | 4 | 0 | ? | 0.99 | 0 |
| $x^{\left(4\right)}$ | 0 | 0 | 5 | 4 | 0.1 | 1.0 |
| $x^{\left(5\right)}$ | 0 | 0 | 5 | ? | 0 | 0.9 |
Stanford University, Machine Learning, Andrew Ng, Coursera
Week 9: Anomaly Detection, Recommender Systems Part 1
Dataset $\left\{ x^{\left(1\right)} ,\, x^{\left(2\right)} ,\,\dots,\, x^{\left(m\right)} \right\}$, is $x_\mathrm{test}$ anomalous?
Model $p\left(x\right) \begin{cases} \lt \varepsilon & \rightarrow \textsf{flag anomaly} \\ \geq \varepsilon & \rightarrow \textsf{OK} \end{cases}$
Stanford University, Machine Learning, Andrew Ng, Coursera
Week 8: Unsupervised Learning, Dimensionality Reduction
training set: $\left\{ x^{\left(1\right)},\, x^{\left(2\right)},\, x^{\left(3\right)},\, \dots ,\, x^{\left(m\right)} \right\}$
step 1: cluster assignment
step 2: move centroid
Stanford University, Machine Learning, Andrew Ng, Coursera
Week 7: Support Vector Machines
Logistic Regression
$h_{\theta}\left(x\right) = \dfrac{1}{1+e^{ -\theta^\mathsf{T}x }}$
if $y=1$, we want $h_{\theta}\left(x\right)\approx1$, $\theta^\mathsf{T}x\gg0$
if $y=0$, we want $h_{\theta}\left(x\right)\approx0$, $\theta^\mathsf{T}x\ll0$
cost function $-y \log h_{\theta}\left(x\right) - \left(1-y\right) \log \left( 1- h_{\theta}\left(x\right) \right)$
Stanford University, Machine Learning, Andrew Ng, Coursera
Week 6: Advice for Applying Machine Learning, Machine Learning System Design
Stanford University, Machine Learning, Andrew Ng, Coursera
Week 5: Neural Networks: Learning
Binary Classification
$y = 0 \textrm{ or } 1$, 1 output unit: $h_\Theta \left(x\right) \in \mathbb{R} \quad s_L=1$
Multi-class Classification
$y \in \mathbb{R} ^K$, K output units: $h_\Theta \left(x\right) \in \mathbb{R} ^K \quad s_L=K$