亚马逊在线分享

① 亚马逊AWS大中华区产学研负责人汤女士将为大家带来AWS的创新文化、职业发展机会、以及AWS提供的一些竞赛机会与免费学习资源；

② 亚马逊AWS大中华区产品部机器学习高级产品经理孟博士将为大家从技术角度分享亚马逊在AI/ML领域的诸多尝试和前沿应用。

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MIT+Fintech Modeling Dynamic User Interests: A Neural Matrix Factorization Approach

Modeling Dynamic User Interests: A Neural Matrix Factorization Approach

Research Context:Boston Globe

  • What they really care about?
    • Content Recommendation:想变得有趣然后留住customer
    • Dynamic User Segementation:
      • 什么是Dynamic User Segementation？
      • Person A :sports/ Person B:politics
      • create profiles, and use this profiles for targeting ads, offers.
      • Company want to figure out your interest and target you and hope you buy their product.
    • Optimize user experience: how to place articles at the front page?

Our broad contribution : A novel neural network for modeling dynamic of user interests

Desired Output: A sample user’s meaningful content consumption journey

Indentify these latent user interests

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MIT+ML06 Intro to Neural Networks

Intro to Neural Networks

Overall Process:

Data-> Feature Extraction -> Classifier

Neural Networks:直接从数据中获取特征进行学习，无需进行特征提取。

That is “turn a feature extraction by hand into a ML task“

简单的分类问题：

  1. get a linear model: h(x)=wTx+b

  2. get a linear model + sigmoid activation: h(x)=σ(wTx+b)

  3. set threshold to do classifier :
    ypredicted={0ifh(x)<0.5\1ifh(x)≥0.5


    在这里，如果是多分类问题呢，我们就去用softmax。

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手动计算深度学习模型中的参数数量

手动计算深度学习模型中的参数数量

我们为什么需要计算DL模型中的参数数量？

More deserve to learn about Efficient Methods and Hardware for DL.

本来呢，代码也可以帮你计算你在模型中所使用的这些trainable parameters of DL models.但是在初期知道如何去计算这些参数对于理解非常有用。

关于以下两类神经网络中的模型参数的计算。

  1. FFNN
  2. CNN

Reference:

Counting No. of Parameters in Deep Learning Models by Hand

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