MIT 6.S91 Introduction Deep Learning Notes

2023-03-09 17:14:58 浏览数 (1)

1.Introduction to Deep learning

  • 震撼，第一节课直接放大招，用自己拍摄的视频和奥巴马合成来介绍这门课程。
  • 不管老师在课程上讲什么，希望你们能真正的思考为什么这一步是重要而且必须的，正是这些思考才能做出真正令人惊讶的突破。

2.Deep Sequence Model

Three way to solve gradient vanish

  • Gated Cells
    • LSTM
      • Forget
      • Store
      • Update
      • Output
  • Attention [[Transformer]]

3.Deep Computer Vision

  • 介绍卷积操作，是一种提取特征的方法生成feature maps（还有其他的方法可以用吗？然后效果还不错）；
    • 与全连接相比的优点；
  • Fast RCNN用于目标检测，怎么实现推荐特定区域图像？
  • 医学图片分割
  • 总结：
    • 原理
    • CNN架构
    • 应用

4.Deep Generative Models

  • what 目标: 来自于一些分布中的训练样本，通过这些样本学习模型来表征这个分布；
  • how 密度估计；神经网络适合来进行高维度表征；
  • why
    • Debiasing: Capable of uncovering underlying features in a dataset
    • Outlier detection: how can we detect when we encounter something new or rare?
  • Latent variable representation:
    • 举例事物的投影，只能看见影子即表象，而被灯光照射的实物是看不见的即隐变量；要做的是通过观察到的投影来对实物进行建模
  • Autoencoder: reconstruction loss
    • 完全是确定性性
  • VAEs：normal prior regularizationreconstruction loss regularization termencoder: q_phi(z|x)decoder: p_theta(x|z)KL-divergence: D(q_phi(z|x)||p(z))
  • GANs
    • make a generative model by having two neural networks compete with each other
    • ⭐️CycleGAN: domain transformations 视频开头的视频就是用这个合成

5.Deep reinforcement learning #

  • Reward:
R_t = r_t gamma r_{t 1} gamma^2 r_{t 2} …
  • Q-function: expected total future reward
Q(s_t, a_t) = E[R_t|s_t, a_t]
  • Policy: to infer the best action to take at its state, choose an action that maximizes future reward
pi^*(s)=mathop{argmax}limits_{s}Q(s, a)
  • Value Learningfind Q(s, a)a = mathop{argmax}limits_{a}Q(s, a)
  • Police Learningfind pi(s)sample asimpi(s)
  • Deep Q Network(DQN)
  • Policy Gradient
  • AlphaGo

6.DL Limitations and New Frontiers

  • limitations
    • Generalization
      • data is important
    • Uncertainty in Deep learning
    • adversarial attack
    • Algorithmic Bias
  • Frontiers
    • encoder
      • many real world data cannot be captured by standard encodings
      • GCN（Graph Convolutional Networks）
    • Automated AI

7. LiDAR for Autonomous Driving

@INNOVIZ

  • Camera Vs LiDAR
    • 互补，视线不好的情况
    • 冗余能保证准确
  • Safety and Comfort

8. Automatic Speech Recognition

@Rev

  • Conformer
  • CTC

9. AI fore Science

Principled AI Algorithms for challenging domains @Caltech

10. Uncertainty in Deep Learning

longer version：NeurIPS 2020 Tutorial @Google AI Brain Team

  • Return a distribution over predictions rather than a single prediction
  • Out-of-Distribution Robustness
    • covariate shift: distribution of features changes
    • open-set recognition: new classes may appear at test time
    • label shift: distribution of label changes
  • sources of uncertainty
    • Model uncertainty
      • 认知上的不确定性
    • Data uncertainty
      • human disagreement label noise
      • measurement noise
      • missing data
  • how to compute
    • BDN
    • GP
    • Deep Ensemble
    • MCMC
  • multi-input and multi output（MIMO）
  • how to communicate with uncertainty?

7-10讲很一般，一个复杂的主题，需要将背景讲清楚，公司讲东西也没啥具体细节。

深度学习

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