【优先队列】百度2018校招编程题—序列合并

0x00 前言

又一次当枪手的经历,但是,说实话好久没敲C++了有些手生,一个是freopen传参是啥来着想半天没想起来,一个是居然忘记优先队列的pop是不return的了……

此题为:

  • 百度2018校招
  • 机器学习/数据挖掘/自然语言处理方向
  • 编程题 第2题
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【Pytorch】Torch_Basic_Learning

前言

本篇为在.ipynb页面上的自学尝试记录,
可以在本人的个人主页上查看或下载自行测试,关于Windows10如何配置Pytorch请移步前篇文章即可~

  • 传送到 个人主页 (Github Pages)
  • 传送到 个人主页 (国内镜像源,加载速度稍快)
  • 传送到 Ipynb的查看或下载

Pytorch

  • Tensor computation (like numpy) with strong GPU acceleration
  • PyTorch is an optimized tensor library for deep learning using GPUs and CPUs.
  • It has a CUDA counterpart, that enables you to run your tensor computations on an NVIDIA GPU with compute capability >= 3.0.
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【Pytorch】Windows10下配置Pytorch环境

0x00 前言

前言什么的也懒得说了……
总之:
听说你Pytorch很牛,
不乐意让我Windows用,
而我又听说pytorch用来训练模型超好用,
不仅没头脑而且不高兴!我要在我的windows上配一个!

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Generative Adversarial Nets

Problem Restatement

This part mainly introduces the principle and realization method of GAN (Generative Adversarial Nets), GAN is proposed by lan.J et al. In 2014, they propose a new framework for estimating generative models via an adversarial process, in which simultaneously train two models: a generative model G that captures the data distribution, and a discriminative model D that estimates the probability that a sample came from the training data rather than G. The training procedure for G is to maximize the probability of D making a mistake. This framework corresponds to a minimax two-player game. In the space of arbitrary functions G and D, a unique solution exists, with G recovering the training data distribution and D equal to 1/2 everywhere. In the case where G and D are defined by multilayer perceptron’s, the entire system can be trained with backpropagation. There is no need for any Markov chains or unrolled approximate inference net-works during either training or generation of samples[1].

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【O2OTM】你的搜索透露了你的喜好——跨模态推荐

@(关键词)[跨模态,推荐系统,O2OTM,可解释性,机器学习]

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论文地址:http://mldm.ict.ac.cn/platform/pweb/academicDetail.htm?id=94

论文翻译:http://blog.csdn.net/okcd00/article/details/51814745

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