项目

三天内和两个队友合作完成了一个项目,基本是零基础,最后还能有展示的Demo,感觉很棒。总结这次项目(NLP)的开发流程如下

开发流程

  • 选题+创意构想(头脑风暴)
  • 数据集分析+数据可视化
  • 数据清理
  • 模型构建(先有个baseline,后面可以大家一起构想;baseline应该先满足end2end)
  • Tuning (这个可以一直进行,但是要保证其它部分有充足时间)
  • Demo (手机App>=网页>电脑App)
  • Poster + PPT
  • Presentation

时间顺序

  • Prepare
    • 选题+创意构想
    • 创建Github仓库
    • 创建Google文档
    • 数据集分析+数据可视化
  • Preprocessing
    • 数据清理
    • Baseline模型
    • 完成end2end
  • Model
    • Model设计
    • Demo设计
  • Tuning
    • Model优化
    • Demo上线
  • Presentation
    • Poster制作
    • PPT制作
    • Presentation准备

讲座笔记

Day 1

Innovation

Demo

  • 每个词做classification->颜色
  • 生成
  • PCA降低维度-分析四个维度的合理性/新标准
  • 社交网站分析
  • Demo-你说我猜

Model

  • Classification
  • 颜文字

Conclusion

  • How to use gcp
  • Design the project
  • pandas data cleaning
  • tensorflow keras API
  • cnn and rnn model for end2end

Day 2

machine learning review

  • Size
    • training set should be 10 size of trainable parameter
  • Practical advice
    • look into outliers and understand why
    • inspect raw examples instead of aggregation
    • checking slices of data
    • be careful about any filtering
    • don’t re-use hashing function
  • What else can we do
    • Shuffle data
    • Normalize data, maybe
    • Sample your data i.i.d.
  • Overfitting/underfitting: model capacity v.s. data set size
  • model validation
  • popular metrics
    • accuracy
    • Logarithmic loss
    • precision & recall
    • TPR & FPR
    • P-R/ROC AUC
    • F1
  • avoiding overfitting
    • drop-out
      • Adam optimizer doesn’t work
    • early stopping
    • regularization
    • moer training data or smaller model
  • gradient clipping
  • smart initialization

AutoML

  • bottelneck
    • data? –NO
    • computation? –NO
      • AI computations doubled every 3.5 months
    • ML expertisee
      • -> AutoML

Semantic Scene and Object Segmentation

  • automatically interpret the contents of an image or video
    • annotations for the entire image
    • annotations from a predefined set of class labels
      • Boundary
      • Object
      • Relations
  • to describe many aspects of the scene

Semantic scene segmentation

  • Image->(bottom up) Interpretation <-(top down) prior knowledge
  • Deep ConvNets (Deep network for image classification)
  • Label representation
  • Upsampling

Semantic instance segmentation

Day 4

Pose Estimation, 3D Vision, and Deep RNN

  • materials: ppt
  • PR->AI
    • 2012 recognition
    • 2014 position detection
    • 2015 sliping
    • 2017 parsing

3D Vision

  • Essay: Segmentation on Point Clouds
    • 3D CNN? Huge computation work
    • Point Net
      • Order Invariance (max/mean)
      • (x,y,z)->TNET->feature for single->merge
    • Point Net++
    • SIFT算子
  • Problem: Rotation
    • Spherical Convolution

Mult-Person Pose Estimation

  • Bottom-Up: First detect all the joints then associate them according to some edge cost functions
  • Top-Down: First detect each human then do singlke person pose estimation
  • AlphaPose
  • CrowdPos
  • PoseFlow

NLP Processing

  • NLP complete
    • 对话系统
    • 机器翻译
    • 阅读理解
    • 智能金融
      • kensho
    • 智能医疗
    • 智能司法

中文分词和词性标注

  • 基于匹配的方法
    • 正向最大匹配法
    • 逆向最大匹配法
    • 最少切分法
  • 分词问题
    • 分词歧义
      • 交集型
      • 组合型
    • 分词标准
    • 未登录词的识别问题
  • Viterbi Alogrithm
  • 机器之心

Interpretable

  • Safety, Trust, Policy and Regulation
  • 实际中,测试集与训练集差别大
  • What I can’t explain simply, I don’t understand

Dataset Analysis

  • Visualization
    • facets
  • Example: 检测坦克(晴天、阴天),鲸鱼(水花),相关性的无关信息

Model Building

  • Interpretability
    • Decision Trees
    • Linear Models
    • Case/Prototype Based
    • Sparsity

Model Analysis

  • 深度神经网络学到了什么?
    • 基本原理:模板匹配。分层的基于余弦相似度的碎片化的模板匹配
    • 逐点模板匹配无法保证放缩和旋转的不变形,深度神经网络也不能
  • Feature Visualization by Optimization
    • Regularization - Frequency Penalization/Tranmsformation Robustness/Lnearned Prior+Transformation Robustness
    • github
  • Attrition/Saliency Maps
  • Gradient Backpropagation
    • DeConvNet
    • Gradient
    • Guided backprop
  • Class Activation Maps(CAM)
  • Distill network to tree
  • TFMA: Tensorflow Model Analysis

Interactive Machine Learning

  • Know the boundary of a model
  • Google
    • Voice assistant
    • Translation
    • Smart reply
    • recommendation
  • Insufficient Interaction Design
    • Anki
    • Lynnette
  • Insufficient Model
    • DragonBox 12+
  • Cost and Scalability of Undersatanding Users
    • Computer’s view of users
    • Dedicated intelligent hardware
  • effective mobile interfaces
    • sensing techniques
    • learning algorithms
    • interaction design
  • Example
    • CourseMIRROR
      • Sample Reflection Question
      • Two Objective Phrase Summarization
    • AttentiveLearner
    • ToneWars

Speech Technology