AI Past Courses by J.-L. Liu
工業革命:動力1.0 (1760),電力2.0 (1870),數位3.0 (1945),智力4.0 (2016 AlphaGo)
AI 聽說讀寫食衣住行育樂醫金…, ABC.
l AI abc: An Introduction to Machine Learning
l Gradient Descent and Backpropagation in Machine Learning (Automatic Differentiation: Forward & Reverse Modes, Jacobian)
l Convolution in Machine Learning (Convolution)
Part I Computer Programming (Browse and Use) GitHub
1.
Colab:
Proj1:
tf3-MNIST.py,
keras_tensorboard.ipynb
(Tutorial)
A.
Style
Transfer (YouTube2,
Code2),
B.
Time
Series (YouTube3),
C.
YOLO
(YouTube4,
Code3),
D.
Stock
(YouTube5,
Code4),
E.
Game
(OpenAI
RL Code5)
2. TensorFlow, TensorBoard (YouTube1)
3. PyTorch (PyTorch Autograd, PyTorch入門, MNIST.ipynb, MNIST.py)
A. Cloud Computing by Colab: Google Chrome => Google Account => Google 雲端硬碟 => Click tf1.ipynb => Upload tf1.ipynb to Google 雲端硬碟 => Click on tf1.ipynb => Install Colab Notebooks => Run tf1.ipynb (Done!)
B.
Local
Computing by jupyter:
Install Anaconda3-4.2.0
(or
more Anaconda) =>
Anaconda Navigator => Environments => Install TensorFlow
=>
Home => Launch jupyter => jupyter => Files on 筆電
=>
Click on tf1.ipynb =>
Run tf1.ipynb (Done!)
How to run py code on jupyter: tf1.py =>
Creat a new file tf1.ipynb with only one line “import tf1” =>
Run the cell of “import tf1” (Done!)
C. Install PyTorch via Anaconda: Click Anaconda Prompt => >conda install PyTorch -c PyTorch => (take a while) => >pip install torchvision => Try 1. import numpy as np 2. import torch 3. x = torch.empty(5, 3) 4. print(x) on jupyter (Done!)
D. Local Computing by PyCharm:
1. tf1.py: # Put this file tf1.py in D:\AI\TF (in English, no Chinese). # Install Anaconda3-4.2.0 => Install PyCharm Community Edition 2017.1.5 for Windows => PyCharm => Create New Project => Location: D:\AI\TF => Interpreter: Click on Create Conda Env (Far Right Button) => Click Create => Click File => Open tf1.py => Tools => Python Console => >>> import pip => >>> pip.main(['install', 'tensorflow']) => Wait for >>> => Click the green triangle button (Run tf1). Done! # See YouTube Link for more if needed. 2. tf2.py: # How to use TensorBoard. # PyCharm => View => Tool Windows => Terminal => Click the green + sign in the Terminal Screen => (C:\Users\Jinn\Anaconda3\envs\untitled1) D:\AI\TF> tensorboard --logdir="./graphs" --port 6006 => Copy http://Jinn-PC:6006 => Google Chrome => Paste http://Jinn-PC:6006 to Chrome's http address => GRAPHS (the graph of a, b, Add) 3. TF mnist 1.0 (tf3-MNIST.py) => # Google Chrome => http://Jinn-PC:6006 => GRAPHS => # Error: matplotlib not installed => # PyCharm => File => Setting => Project: TF => Project Interpreter => + => matplotlib
=> Specify version => Install # Save zout_MNIST_1.0.png
Part II Supervised Learning (Read and Work)
1. A Simple Learning Model: Classification, Target, Hypothesis, Training Data, Learning Algorithm, Weights, Bias, Supervised and Unsupervised Learning
2. Google Tutorial for ML Beginners: Image Recognition, MNIST, Softmax Regression (92%), Cross Entropy, Gradient Descent, Back Propagation, Computation Graph (TF mnist 1.0)
3. Tensorflow and Deep Learning I (by Martin Gorner) : Deep Learning Network, ReLU, Learning Rate (98%), Overfitting, Dropout (98.2%), Convolutional Neural Network (99.3%) (TF mnist 3.1)
4. Tensorflow and Deep Learning II (by Martin Gorner) (RNN1): Batch Normalization (99.5%) (TF mnist 4.2), Data Whitening, Fully Connected Network, TensorFlow API, MNIST Record (Kaggle: 100%), Recurrent Neural Network, Deep RNN, Long Short Term Memory, Gated Recurrent Unit, Language Model
Part III Theories of Deep Learning
1. Lectures at MIT (Book: Deep Learning by Goodfellow, Bengio, Courville)
Part IV Reinforcement Learning (by David Silver, YouTube, Book)
1. Introduction to Reinforcement Learning
3. Planning by Dynamic Programming
6. Value Function Approximation
8. Integrating Learning and Planning
9. Exploration and Exploitation
10. Case Study: RL in Classic Games
More AI Links …
[1]* KDD Cup Competition, Data Science Game
[2] Google TensorFlow Frontiers, Non-Experts, ML APIs, Dev Summit 2017
[3] OpenAI, OpenAI Universe
Part V AI Seminar
1. 2017.08.01: Udacity Self-Driving Project (Simulator), Auto1-Robocar-Lidar, Auto2-CNN-KITTI-TORCS, Auto3-CNN-OSM-GSV
2. 2017.08.08: D. Lee, Introduction to Humanoid Robotics
3. 2017.09.11: 陳冠霖, Introduction to Visual Common Sense for Autonomous Driving
4. 2017.09.25: Auto4-DeepQNet-Atari (DQN1), Auto5-DQN-TORCS (Patent, TFCode, DQN2, OpenAI Gym (Paper))
5. 2017.09.25: 劉冠漢, At First Glance for Direct Perception and Simulator TORCS in Autonomous Driving
6. 2017.11.13: 朱沛全, Magenta, AI Experiment, Google, Generate Your Own Sound with NSynth
7. 2017.11.27: 劉昌倫, Continuous control with deep reinforcement learning-TORCS
8. 2017.11.27: D. Lee, Introduction to Caffe Framework (Caffe1, Caffe2, Caffe-Code)
9. 2018.01.08: Auto6-VoxelNet (RPN)
10. 2018.03.02: Auto7-TORCS (GoogLeNet-YouTube, GoogLeNet-Tensorflow)
11. 2018.03.19: Sydney Lin, Google USA, My Experience to and at Google
12. 2018.03.19: 劉冠漢, A very simple example of building new tracks
13. 2018.03.26: D. Lee, Algorithm for distance indicators
14. 2018.04.09: 廖鴻志, Introduction to GoogLeNet
15. 2018.04.16: 陳冠霖, Installation of DeepDriving
16. 2018.04.23: D. Lee, TORCS AI driving data for opponent distances
17. 2018.04.30: 劉昌倫, Deep Learning Algorithm for Autonomous Driving using GoogLeNet
18. 2018.05.14: Auto8: World Models (RL, RNN, VAE, MDN, V, M, C, OpenAI Gym, Car) (Code)
19. 2018.11.02: D. Lee, Tasks and ML approaches of autonomous driving
20. 2018.11.09: 陳冠霖, Implementation of DeepDriving (2018 Open Data 分析競賽)
21. 2018.11.23: 王偉誠, Robot Path Planning with TSP by Solving Pointer Networks
22. 2018.12.21: D. Lee, Development of a new controller in DeepDriving
23. 2018.12.28: 劉昌倫, Using Keras and Deep Deterministic Policy Gradient to Play TORCS
24. 2019.01.04: D. Lee, The new controller and proposed 5 indicators
25. 2019.04.12: Google TossingBot (Website, Paper, PyTorch, CNN, RL, Physics)
News
1. 2021.11.12: 國際自駕車趨勢與挑戰
1. 2020.12.24: 新創公司Nuro取得加州第一張許可
2. 2020.12.23: 蘋果打造自駕車輛傳於2024年量產
3. 2020.12.17: 聯發科成為MLCommons聯盟的創始成員
4. 2020.11.13: 語調激似真人!南韓首位AI主播亮相
5. 2020.06.26: 投資自駕車最大手筆 亞馬遜12億美元收購Zoox
6. 2019.09.17: 威盛用智慧型手機駭進 CRV 變自動駕駛
7. 2019.08.01: 「自駕腳踏車」AI 跨出了超越人類智慧的第一步!
8. 2019.06.14: AWS首度在臺北舉行自動駕駛迷你車比賽
9. 2019.06.12: 交大AI團隊研發自駕車發嵌入式AI物件辨識系統
10. 2019.05.21: 2019全國智慧製造大數據分析競賽 (總獎金220萬)
11. 2019.04.24: 國家隊自駕科研成果吸睛 工研院自駕車南寮上路!
12. 2019.03.29: Google機器人TossingBot
13. 2019.03.28: 從AI三大趨勢看台灣廠商的發展策略
14. 2019.02.19: 2019中華郵政大數據競賽 (總獎金100萬)
15. 2019.02.19: Google、亞馬遜、臉書投入AI晶片研發
16. 2019.02-18: Another AI Winter?
17. 2018.12.05: AI幫醫師癌症治療決策下得更快
18. 2018.11.09: 教育部智慧聯網專題實務競賽
19. 2018.10.02: 2018 Open Data 分析競賽
20. 2018.09.20: 全球人工智慧新創的募資概況
21. 2018.09.18: 日盛黑客松競賽 (總獎金25萬)
22. 2018.08.31: 自駕車的未來與挑戰 胡竹生、王傑智(交大電機系與工研院機械所)
23. 2018.07.17: 教育部全國智慧製造大數據分析競賽 (總獎金220萬)
24. 2018.05.32: Where should I start learning AI?
25. 2018.05.16: Data Science Game 2018 (法國)
26. 2018.05.12: Running Robot, Robot Dog
27. 2018.04.26: 科技部創新創業激勵計畫
29. 2018.03.02: 20 Math Books for Machine Learning
30. 2018.02.02: 鴻海搶攻AI 五年內砸100億
31. 2017.12.31: Deep Learning Achievements of 2017 (Part 2)
32. 2017.12.31: Deep Learning Achievements of 2017 (Part 1)
33. 2017.12.05: How can I learn the big picture of Machine Learning?
34. 2017.11.27: 聯發科廣招AI人才 投資平民自駕車AutoX (AutoX)
35. 2017.11.19: 英政府力推無人車相關產業 希望2021上路
36. 2017.10.28: What is deep reinforcement learning?
37. 2017.10.26: Can AI find prime numbers?
38. 2017.10.25: AI人才稀缺 科技廠一擲千金
39. 2017.09.18: AI Pioneer Says We Need to Start Over
40. 2017.06.14: 蘋果開發自動駕駛系統,那是「所有AI之母」
41. 2016.08.16: Is deep learning overhyped?
Part VI Self-Driving Cars
1. Introduction to Self-Driving Cars
l Carnegie Mellon U 1989, CMU Vehicle, Computer
l comma.ai openpilot 2018, commaai, comma-GitHub, openpilot
l Tesla Autopilot 2019, auto vs open
l Self-Driving Car, Autonomous Car
2. End-to-End Learning for Autonomous Driving
3. Project 1: Steering Angle
l *comma coding
l Toyota Dynamic Radar Cruise Control, Adaptive Cruise Control,
4. Project 2: Lane Detection
l Toyota Lane Tracing Assist, Lane Centering
5. Project 3: Speed Prediction
6. Project 4: Localization
l Theory: GNSS Processing, Trilateration, Least Squares,
7. Driving Video Dataset
8. Hardware and Software in Self-Driving Cars
9. Longitudinal and Lateral Control
10. CAN Bus Protocol
11. Environment Perception
12. Traffic Signs Detection
13. Pedestrian Detection
14. How to ensure the safety of Self-Driving Cars
comma Coding
Introduction: comma.ai, comma two (CTwo), *openpilot (OPGit, OP1, OP2, OP3, OP4)
Machine Learning: AIabc, CNN, RNN
NN: Keras, Krs1, Krs2, Krs3, Krs4, 10CNNs, ResNet, OPNet, RetinaNet, Yolact, Demo, Zoox
Homework
HW1:
Run and read (A)
JL1.py
(OPNet,
Leon,
LeonB,
Shen)
(B)
TT1.py
(Yola20, IS1,
IS2,
Yolo18,
RetN17,
SSD15,
OF13)
HW2:
Run and read mnist.py
(py2pb),
mobile.py,
retina.py
HW3:
Install Ubuntu
20.04 by VMware
(Guide1,
Guide2,
Guide3)
HW4:
Install and run openpilot
(UI). Read unlogger.py,
ui.py.
HW5:
Read view_steering_model.py
HW6: Read train_steering_model.py and server.py
Projects
Project
1:
Steering and Lane
Detection:
HW1A, HW5, HW6
Project
2:
Instance Segmentation: HW1B, HW2, SNPE
(pb2dlc)
Project
3:
Deployment: HW1A, Shen,
SNPE,
CTwo,
WB1
Project
4:
Control: Project 3, OP2,
Tune,
Panda (PD1),
CAN
2017 Summer Course
人工智慧程式設計Artificial Intelligence Programming
A Short Scientific Programming Course for All Students
Prerequisites: Undergraduate Calculus and Programming
Time: 1 ~ 4pm, Tuesdays, July 4 ~ Aug. 8 (6 weeks), 2017
Place: 校本部綜三館203
Please register online 線上註冊 for this course.
Instructor: Jinn-Liang Liu 2017.5.21
2017 Fall Course
人工智慧程式設計Artificial Intelligence Programming
Lecture: PHYS物 504, 1:20pm, Mondays (2017.9.11 – 12.25)
Seminar: PHYS物 504, 2:45pm, Mondays (2017.9.11 – 12.25)
Grading: Coding Projects and Reports 100%
Reports: Send reports in ppt (short) and pdf (long) files to jinnliu@mail.nd.nthu.edu.tw by 10/15; 11/15; 12/25.
2018 Winter Seminar
人工智慧專題Topics in Intelligence Programming
Seminar: GEN II綜二402, 2pm, Mondays (Jan 8, Jan 22, Feb 5)
2018 Spring Course
人工智慧專題 Topics in Artificial Intelligence
Seminar: PHYS物 501, 1:20 - 4pm, Mondays (2018.2.26 – 6.11)
Grading: Coding Projects and Reports 100%
Reports: Send reports in ppt (short) and pdf (long) files to jinnliu@mail.nd.nthu.edu.tw by 3/12; 4/2; 4/22; 5/13; 6/3.
2018 Summer Short Course
機器學習簡介 Introduction to Machine Learning
A Short ML Programming Course for All Students
Prerequisites: Undergraduate Calculus and Programming
Time: 10am ~ 12, 1:30 ~ 4:30, July 16 (Monday) & July 19 (Thursday), 2018
Place: GEN III綜三館837
Please register online 線上註冊 for this course.
Course Info: Google Jinn-Liang Liu for details. 2018.5.25
2018 Summer Seminar
人工智慧專題 Topics in Artificial Intelligence
Seminar: GEN II綜二計科所402 (非學科所402), 2:30pm on June 11, 25; July 9, 23, 30; Aug. 27; Sept. 3, 7.
2018 Fall Course
機器學習 Machine Learning
Lecture: ENG1工一 217, 1:20 – 1:35pm, Fridays (2018.9.14 – 2019.1.4)
Seminar: ENG1工一 217, 2:50 – 4:05pm, Fridays (2018.9.14 –2019.1.4)
Reports: Do Proj1, 2, 3, 4 and Send reports in ppt (short) and pdf (long) to jinnliu@mail.nd.nthu.edu.tw by Wednesdays 10/10; 11/14; 12/26.
2019 Winter Seminar
人工智慧專題 Topics in Artificial Intelligence
Seminar: GEN II綜二計科所, 3:00-5:00, Wednesdays (2019.1.9 (綜二A402), 1.16 (綜二A813), 1.23 (綜二A813)).
2019 Spring Course
深度學習 Deep Learning
Lecture: ENG1工一 217, 1:00 – 2:50pm, Fridays (2019.2.22 – 2019.6.14)
Seminar: ENG1工一 217, 3:00 – 3:50pm, Fridays (2019.2.22 – 2019.6.14)
Reports: Do Proj1 and a project of your choice and Send reports in ppt (short) (Demo) and pdf (long) to jinnliu@mail.nd.nthu.edu.tw (email 標題:姓名 學號 Report 1, 2, or 3) by Wednesdays 3/27; 5/1; 6/12 (and 5-Minute Presentation in mp4). 5-Minute Presentations (Demo) on 5/24; 5/31. Demos and Discussions 6/14.
2019 Fall Course
深度強化學習 Deep Reinforcement Learning
Lecture and Seminar: ENG1工一 217, 1:20 – 4:10pm, Thursdays (2019.9.12 – 2020.1.2)
Reports: Send reports in ppt (short) (Demo) and pdf (long) to jinnliu@mail.nd.nthu.edu.tw (email 標題:姓名 學號 Report 1, 2, or 3). Report 1 on Proj1, due 10/3. Report 2 on Your Project, due 11/14. Report 3 on Your Project AND Presentation File mp4, due 12/19. 5-Minute Presentations (Demo) on 12/26 and 1/2.
2020 Spring Course
Topics in Machine Learning 機器學習專題
Online Course Meetings on MS Teams. How to join? Check your email sent by Teams.
Online 1:20 – 4:10pm, Wednesdays (2020.4.1 – 5.6)
ENG1工一 209, 1:20 – 4:10pm, Wednesdays (2020.3.4 – 3.25; 5.13-6.17)
Requirements: Laptop, Python, Calculus, Physics, Lots Reading, Q&A
Send reports in long docx (demo) and short ppt (demo) or short mp4 (demo) to jinnliu@mail.nd.nthu.edu.tw (Email Subject: Name, Student ID No., Report 1, 2, or 3.)
Project Reports 1, 2, and 3 are due on 4/8, 5/13, and 6/17, respectively. 15-Minute Presentations (in mp4) on 6/17.
2020 Summer Seminar
Machine Learning for Self-Driving Cars
Online (mostly) Seminar Meetings on MS Teams. How to join? Check your email invited by me and sent by Teams.
Online 2 – 5 pm, Wednesdays (2020.6.24 – 9.2) *comma coding
2020 Fall Course (Seminar)
Machine Learning for Self-Driving Cars
ENG1工一 209, 1:20 – 3:10 (3:20 – 5:20) pm, Wednesdays (2020.9.16 – 2021.1.6)
Send reports to jinnliu@mail.nd.nthu.edu.tw (Email Subject: Name, Student ID No., Report 1, 2, or 3.)
Report 1: ppt (demo) by 10/21. Report 2: ppt by 11/25. Report 3: ppt and docx (demo) by 1/6.
Presentations on 12/30 and 1/6. *comma coding
2021 Winter Seminar
Machine Learning for Self-Driving Cars
校本部綜二館 GEN II 813, 2 – 5 pm, Wednesdays (Jan 20, 27; Feb 3, 2021) *comma coding
2021
Spring Course
Topics in Deep Learning 深度學習專題 *comma coding
Online 2:00 – 5:00pm, Wednesdays (2021.3.3 – 6.16) Check your email sent by Teams.
On Campus GEN II綜二A813, 1:20 – 5:10pm, Thursdays (2021.3.4 – 6.17)
Send reports to jinnliu@mail.nd.nthu.edu.tw (Email Subject: Name, Student ID No., Report 1, 2, or 3.)
Report 1: ppt (demo) by 3/31. Report 2: ppt by 5/5. Report 3: ppt and docx (demo) by 6/16.
2021 Fall Course
Deep Learning 深度學習 *comma coding (Report)
Online/On Campus Class 1:20 – 4:10 pm, Wednesdays, 10/6-1/5 by Teams/in GEN II綜二A813
Online Class 1:20 – 4:10 pm, Wednesdays, 9/15-9/29 by Teams
Please email me for Teams Link, if you did not register the course.
Send reports to jinnliu@mail.nd.nthu.edu.tw (Email Subject: Name, Student ID No., Report 1, 2, or 3.)
Report 1: ppt (demo) by 10/27. Report 2: ppt by 12/1. Report 3: ppt and docx (demo) by 1/5.
comma Coding
Introduction: comma.ai, comma two (C2), *openpilot (OPGit, OP1, OP2, OP3, OP4)
Machine Learning: AIabc, CNN, RNN
NN: Keras, Krs1, Krs2, Krs3, Krs4, Krs5, 10CNNs, ResNet, Yolact, OPNet* (OPN)
Homework
HW1:
Do Install
Ubuntu and OP, Step
1, Step
2
HW2:
Run and Read modelA1.py,
modelB1.py
(Demo)
Projects
Project
A:
Instance
Segmentation (Demo,
IS,
comma10k,
Cityscapes,
Apollo)
Project
B:
Path
Prediction
(comma2k19)
AI Web Seminar
4:15 – 5:30 pm, Wednesdays, 2/16 – 6/8, 2022
Welcome to join us!
It is about OP coding on self-driving cars.
Please send me an email for a Microsoft Teams link to join.
2022 Spring Course
Machine Learning 機器學習 *OP Coding
1:20 – 4:10 pm, Wednesdays, 2/16-6/8, GEN II綜二A813 on-campus or online (Teams link sent by email)
Send reports (in ppt, demo) on Project B5 to jlliu@mx.nthu.edu.tw (Email Subject: Name, Student ID No., Report 1, 2, or 3)
Report 1: Due by 3/30. Report 2: 5/4. Report 3: 6/8.
Lecture Notes
· *AI abc: An Introduction to Machine Learning
· *Gradient Descent and Backpropagation in Machine Learning (Automatic Differentiation: Forward & Reverse Modes, Jacobian)
· *Convolution in Machine Learning (Convolution)
Part I Supervised Learning
1. A Simple Learning Model: Classification, Target, Hypothesis, Training Data, Learning Algorithm, Weights, Bias, Supervised and Unsupervised Learning
2. Google Tutorial for ML Beginners: Image Recognition, MNIST, Softmax Regression (92%), Cross Entropy, Gradient Descent, Back Propagation, Computation Graph (TF mnist 1.0)
3. *Tensorflow and Deep Learning I (by Martin Gorner): Deep Learning (98%), ReLU, Learning Rate, Overfitting, Dropout (98.2%), Convolutional Neural Network (CNN, 99.3%) (TF mnist 3.1)
4. *Tensorflow and Deep Learning II (by Martin Gorner) (RNN1): Batch Normalization (99.5%) (TF mnist 4.2), MNIST Record (Kaggle: 100%), Recurrent Neural Network, Deep RNN, Long Short Term Memory, Gated Recurrent Network
Part II Self-Driving Cars
1. Introduction to Self-Driving Cars
· Carnegie Mellon U 1989, CMU Vehicle, Computer
· comma.ai openpilot 2018, commaai, comma-GitHub, openpilot
· Tesla Autopilot 2019, auto vs open
· Self-Driving Car, Autonomous Car
2. OP Coding
3. Data: comma2k19, comma10k, Cityscapes, Apollo,
4. How to ensure the safety of Self-Driving Cars