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<div class="post-preview "
style="background-image: url('/2018/11/19/AI迷航/2018-08-31 184420.png')">
<a href="/2018/11/19/AI迷航/">
<h2 class="post-title">
AI迷航
</h2>
<p class="post-meta">
<i class="fa fa-calendar" aria-hidden="true"></i> 2018-11-19
</p>
<div class="post-content-preview">
<p>
1. 第一人称作品大部分都会沦为灾难。
本书也无法避免。
或者,温和一点来讲,写得好的第一人称作品实在难得,而本书不在此列。
主角是由情节驱动而不是人物本身(当然这也无可厚非),让人有种被推着走的不适感。人物即使想要做出什么改变,也并非是性格使然而更像是一种基于推动情节的作用。
程复更像是作者用..
</p>
</div>
</a>
</div>
<hr>
<div class="post-preview "
style="background-image: url('/2018/09/19/RL-paper-reading-1/2018-09-18 221400.jpg')">
<a href="/2018/09/19/RL-paper-reading-1/">
<h2 class="post-title">
Reinforcement Learning Algorithms Part1:DQN
</h2>
<p class="post-meta">
<i class="fa fa-calendar" aria-hidden="true"></i> 2018-09-19
</p>
<h3 class="post-subtitle">
Nature DQN, 2015, DeepMind
</h3>
<div class="post-content-preview">
<p>
Nature DQNDeepMind, 2015Paper
summary
use CNN to estimate state value function
adopt replay buffer to train agent
use separate target network to com..
</p>
</div>
</a>
</div>
<hr>
<div class="post-preview "
style="background-image: url('/2018/05/02/VAE-part2/VAE.jpg')">
<a href="/2018/05/02/VAE-part2/">
<h2 class="post-title">
Introduction to VAE(Variational Auto-Encoder) Part2
</h2>
<p class="post-meta">
<i class="fa fa-calendar" aria-hidden="true"></i> 2018-05-02
</p>
<h3 class="post-subtitle">
变分自编码器简介(二)
</h3>
<div class="post-content-preview">
<p>
VAE的简要介绍Part1从算法角度入手,以mnist数据集为例介绍VAE的输入输出,损失函数和基本理论,以及VAE的常见应用,附带Pytorch实现的VAE代码;Part2着重于概率论的理论分析,涉及到流形学习,稍微提到了与VAE相似的CAE。
原理VAE对隐层输出增加长约束,在隐层采样过程中..
</p>
</div>
</a>
</div>
<hr>
<div class="post-preview "
style="background-image: url('/2018/05/01/intern-interview/inter.jpg')">
<a href="/2018/05/01/intern-interview/">
<h2 class="post-title">
一个记录
</h2>
<p class="post-meta">
<i class="fa fa-calendar" aria-hidden="true"></i> 2018-05-01
</p>
<h3 class="post-subtitle">
Intern Interview
</h3>
<div class="post-content-preview">
<p>
流程一面全过,二面后开始刷人,如果前两面都是negative,就没有三面。三面后当场可以从HR那里得到大致结果。
一面会根据简历问一些相关问题,都不难,比如机器学习相关的问了聚类和k-means。然后手写代码。二面跟一面差不多,简要介绍以后直接写代码。三面是主管面,内容取决于面试官的性格,可能不..
</p>
</div>
</a>
</div>
<hr>
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style="background-image: url('/2018/04/30/VAE-version1/VAE.jpg')">
<a href="/2018/04/30/VAE-version1/">
<h2 class="post-title">
Introduction to VAE(Variational Auto-Encoder) Part1
</h2>
<p class="post-meta">
<i class="fa fa-calendar" aria-hidden="true"></i> 2018-04-30
</p>
<h3 class="post-subtitle">
变分自编码器简介(一)
</h3>
<div class="post-content-preview">
<p>
VAE的简要介绍Part1从算法角度入手,以mnist数据集为例介绍VAE的输入输出,损失函数和基本理论,以及VAE的常见应用,附带Pytorch实现的VAE代码;Part2着重于概率论的理论分析,涉及到流形学习,稍微提到了与VAE相似的CAE。
输入输出以mnist数据集为例,输入为m维,输出..
</p>
</div>
</a>
</div>
<hr>
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style="background-image: url('/2018/04/20/numpy-tips/1524535685939.jpg')">
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<h2 class="post-title">
Tips for Numpy
</h2>
<p class="post-meta">
<i class="fa fa-calendar" aria-hidden="true"></i> 2018-04-20
</p>
<div class="post-content-preview">
<p>
create arrayarr = np.arange(10)np.full((3, 3), True, dtype=bool)np.ones((3,3), dtype=bool)#> array([[ True, True, True],#> [ True, T..
</p>
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style="background-image: url('/2018/04/17/new-blog/1524537463632.jpg')">
<a href="/2018/04/17/new-blog/">
<h2 class="post-title">
RNN(Recurrent Neural Networks)
</h2>
<p class="post-meta">
<i class="fa fa-calendar" aria-hidden="true"></i> 2018-04-17
</p>
<h3 class="post-subtitle">
Simple Introduction to RNN
</h3>
<div class="post-content-preview">
<p>
结构
RNN包含循环,允许信息持久化
前向传播$h^{(t)}$为隐藏状态,$L^{(t)}$为损失函数,$o^{(t)}$是模型输出,$\hat{y}^{(t)}$是真实输出。
t时刻的隐藏状态$h^{(t)}=\sigma(Ux^{(t)}+Wh^{(t-1)}+b)$, $\sigma$为..
</p>
</div>
</a>
</div>
<hr>
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style="background-image: url('/2018/03/03/RL-install-md/1524460859091.jpg')">
<a href="/2018/03/03/RL-install-md/">
<h2 class="post-title">
Install Reinforcement Learning Environment
</h2>
<p class="post-meta">
<i class="fa fa-calendar" aria-hidden="true"></i> 2018-03-03
</p>
<h3 class="post-subtitle">
Mujoco, Gym, Mujoco-py
</h3>
<div class="post-content-preview">
<p>
环境:Ubuntu 16.04, Cuda 9.1, nvidia-390, Python 3.5.2
安装顺序:Mujoco, Mujoco-py, Gym
对应版本:
Mujoco
Mujoco-py
Gym
mjpro150
1.50.1
0.9.6
mjpro131
..
</p>
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