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## 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

## 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 ## 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 • 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
• calssification
• Unsupervised Learning
• Clustering
• Dimensionality Reduction
• Reinforcement Learning • 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 • 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) • 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 convolution 在做 feature extraction

## A Complete Example of CNN ## Classics of CNN

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

• 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

• 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 ? • 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

• 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?

## AI 產業化 • 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

• 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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