阿葛廷

人一定要靠自己!

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靠邀根本在練速打　ＸＤＤＤ

Alan Turing

  • Mathematicain, logician, cryptanalyst, philosopher, theoretical biologist, and computer scientist.

  • The father of theoetical computer science and artificial intelligence

Turing Test (1950)

  • If tester cannot distinguish a machine and a man, this machine can be considered to have “intelligence”.(First time to use)

  • “Imitation Game”

  • AlphaGo as “Master” in end of 2016

  • Google Assistant in 2018 Foogle I/O

Strong AI Weak AI
General task　(AGI=Artifical General intrlligence) Specific task

History: AI in NN

AI: Categories

  • can be understood ?
    • Symbolic AI:
      • Intelligence comes from Human-dfiened Ruile(formal logic)
        • Human can understand adn “explain mathematically”
        • Human Rule <= Human Language <= Symbol => Symolic AI
      • Rule-Base
  • can be gained via learning ?
    • Computational Intelligence
      • Intelligence come from Machine Computation
        • Human can’t understand nor “explain …”
        • Use “Evolutionary Algorithm” as example
      • No “rule”
      • Computation usually links to “learning” and “training”
      • Example: Nerual Network, Fuzzy logics, Genetic Algorithm…
      • Connectionism VS. Symbolimd

Imgur-ai

What is Machine Learning (ML)?

  • ML- Perspective 1: Human Behavior (1/2)
    • Our daily life, we repeat an action: use knowledge to make a decision
    • We expect: make a good decision
    • How to mae a good decision: learn
      • Prior-learning
      • Post-learning
    • How to adjust our knowledg:
      • know gap between good and bad decision
      • Eliminate the gap by adjusting knowledge
  • ML - perspective 1 : Human Behavior (2/2)
    • knowledge, Decision, Learn, Gap, Eliminate
    • knowledge = Intelligence = Model: ML or DL
    • Gap = Difference/Delta = Error/Residual = Loss Function (Objective)
    • Eliminate = Minimize/Optimize: Gradient Descent or Close-Form
    • Eliminate Gap = Learn = Train
      • prior-learning
      • post-learning
  • Deep Learning
    • Classic Machine Learning: example
      • Decision Tree tree
  • ML- Perspective 2: Problem Solving
    • Problem, Data, Model
  • ML- Perspective 3: Software
             flow(algorithm), 
input -->    Parameters,       --->  Output 
             Working Data
  • Software => Algorithm
    • 機器學習演算法是一個 解決問題的架構

Type of Machine Learning

  • Supervised Learning
    • Regerssion
    • calssification
  • Unsupervised Learning
    • Clustering
    • Dimensionality Reduction
  • Reinforcement Learning

ml-chart

  • There are many Method, Approach and Algorithm to realize each Type of Machine Learning. Like DL as below:

    • SL: DNN, CNN, RNN
    • RL
    • UL: GAN*

Machine Learning Summary

  • SL, UL, RL
  • Problem to REsolve
  • Approach
  • Deep Learning: DNN, CNN, RNN, GAN, RL

DL : NN Models / ANN (DNN)

  • ANN: artifical neural network

  • DNN: Deep neural network

  • Artifical Neural Network (ANN)

    • Topolog neural-network

      • Decide (Predict or Inference)
        • x = input
        • w = weight
        • b = bias
        • Activation Func
        • y = output
        • y1, y2,

Learn (Train)

  • learning: designated inpur, desired output
  • Use simplified single perceptron as examle
    • No activation function and treat bias as constant
    • y1 = x1…..

Learn: Symbolic => Numerical

  • 數值分析(numerical analysis): 不是求最正確的答案，而是在合理的範圍（資源、時間等．．．）下求最合理的解。

Learn: Minimize

  • finding minimum: Gradient Descent (a numerical method)
  • 請看我自己 愛心的筆記　：）

Multi-Layer Perceptron (MLP)

mlp-img

  • Softmax Function: exponentially normalized

  • Example: MLP

Chain Rule in Differential

  • single variable:

     y = g(x), z = h(y)
   
 Δx -> Δy -> Δz
   
 dz/dx = dz/dy * dy/dx

  • Backproagation CS231 NN:

Convolution Neural Network (CNN)

  • CNN is a nerwork to imitate Human Vision (ANN: Brain or BNN)
  • Human vision: Discriminate (ANN: Decide and Learn)
  • We

Feature Extraction: Matrix Convolution

cnn-img

convolution 在做 feature extraction

A Complete Example of CNN

cnn-complete-ex

Advanced about Convolution

Classics of CNN

  • Yann LeCun
  • ImageNet/ ImagesNet Large Scale Visual Recognition
    • imagenet

Application of Image Recognition(1/2)

  • Object detection
  • 分類

Application of Image Recognition(2/2)

  • segementation (分割)
  • Semantic Segmentation vs. Instance Segmentation

RNN

rnn

  • 模仿人的記憶
  • 把前一次的結果記錄起來，下一次　input 併近來一起做運算

RNN 時間有關西，CNN 空間有關西

##LSTM and GRU

  • improvement model

Generative Adversarial Network (GAN)

  • 產生器產生出來，分類器分類出來
  • ”” : two models “fights”

Steps of Training

  • Step1: Fix generator G , and

Stes of Training

  • step2:

DL: NN Models / Flow

  • top-level flow: Model, Data, Problem

  • 定義你的問題

Data: Prepare, Pre-process, Analyze

  • 70% 以上，都是在處理 data

  • prepare
    • collect: collect dataset
    • label: label expected output for input data
  • pre-process
    • clean: corrupt, inaccurate, and missing values
    • transform: capture the pattern; easy to manipulate
  • analyze
    • explore: EDA(Explorative Data Analysis) and Visulization
    • feature engineering: Encoding and Selection
  • pre-process
    • split: training set(考古題)/ validation set(練習題)/testing set(大考)
    • normalize: feature scaling, standard score

Data Label Problem

data, data, data 很重要！

Enough Data

Enough Labeled Data



Problem Definition

Metrics

Domain Experts

  • 回去跟　domain 的人多聊聊！
  • 定義一個有意義的問題

AIA: Course

  • python:
  • 機率與統計
  • 機器學習概論(ML)
  • 深度學習(DL)
  • 卷積神經網路與電腦視覺(CNN/CV)
  • RNN
  • NLP
  • 遷移學習TL(transfeom learning) & 生成對抗網路(GAN)

AI: Boom

  • reasons behind the boom

    計算複雜度下降　+ 計算力的提升　–> 人工智慧的黃金交叉！

    • Theories Ready: ANN, DNN, CNN, RNN, RL …
      • DNN: Gradient Descent and Backpropagation
      • CNN: Convolution and NN-ized
    • Hardware Capability: Nvidia GPU + CUDA
    • Big Data: ImageNet and ILSVRC
    • Software Tools / Communities:
      • Open Source

  • Why DL is hotter than ML ? why-dl-is-hotter-than-ml

  • Software Tools

    • Language: python, c++, java
    • framework: Tensorflow, keras, pytorch…
  • Architecture of Development in DL/ML
    • Framework/Package: Tendorflow + keras, PyTorch, CNTK …
    • Language: Python
    • Environment(Execution): Jupyter(julia, Python, R …)
    • Environment(Package): anaconda
    • Hardware: local: pc, Cloud (CPU/GPU): Google, …

  • Software Communities

    • GitHub:
      • type: service, code version control, SCM
    • Kaggle:
      • type: community and platform for data scientists
  • Type of Applications
  1. Problem-driven
    • EX: AOI (瑕疵檢測)
  2. Off-the-shelf
    • 有套件在那邊了，EX: yolo
  3. Goal-driven
  4. Data-driven
    • 要有 data 導向思維
  5. Labor-oriented

Typical Application: 製造業

  • 瑕疵檢測
  • 預測性維護
  • 自動流程控制
  • 原料組合最佳化

Challenge from Data - 1

  • Data, more data, more labeled data!
  • Training data is not the real world => overfitting
  • Model is sensitive to minor change of data

Challenge from Data - 2

  • 有data，沒技術
  • 有技術，沒data
  • 有data，有技術：
    • 不確定這訓練出來的 model　ok?

“Heruistic” and “Empirical”

AI 產業化

Imgur

  • AI infrastucture
  • AI Chipset
  • AI Consultant
  • Product with Ai Functionality

大神們

  • Geoffrey Hinton
    • British
    • Multi-layer neuron network, backpropagation
    • Univ. of Toronto.
  • Yann LeCun(楊立昆)
    • CNN. ML, VS, Robotics
  • Yoshua Bengio

大神們們

  • 黃仁勳
  • 李飛飛
  • 吳恩達

AI

  • 1980: pc

  • 1990: internet

  • 2000: Mobile networking

  • 2010: AIOT

Portability

Computing

Networking

AI ===> Computing ability

Which solves faster

a top modern algorithm on a 1980s processor or a 1980s algorithm running on a top modern model.

Musical AI

  • https://composing.ai/dataset

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