1. 機器學習與深度學習技術簡介 2. TensorFlow/Keras介紹 3. Keras程式架構與流程 4. Neural Network原理與Python實作 (see demo code) 5. DNN實作分類問題-使用Keras (see demo code) 6. CNN實作圖像分類問題-使用Keras (see demo code

1. 文化大學 資訊工程系
主講人: Joseph (艾鍗學院)
18 March 2019
深度學習實戰
2019 艾鍗校園講座 NO.1

2. 1. 機器學習與深度學習技術簡介
2. TensorFlow/Keras介紹
3. Keras程式架構與流程
4. Neural Network原理與Python實作 (see demo code)
5. DNN實作分類問題-使用Keras (see demo code)
6. CNN實作圖像分類問題-使用Keras (see demo code)
Agenda

3. TensorFlow/Keras介紹

4. Deep learning frameworks
Deep learning frameworks offer building blocks for
designing, training and validating deep neural
networks
4

5. TensorFlow/Keras介紹

6. GPU/TPU/FPGA 加速神經網路運算

7. 使用GPU運算加速器
強大的運算資源和GPU等運算加速器可以有效加
速深度學習模型收斂的速度。
以AlphaGo為例，Google DeepMind使用了50個
GPU訓練了3個星期，如果只用1個CPU可能需要
超過30年才能得到相同能力的模型。
https://www.nvidia.com/
7

8. 安裝Tensorflow-gpu 版本流程

9. Open [Anaconda Prompt]
Anaconda安裝keras+tensorflow
conda install keras
conda install tensorflow
conda install tensorflow-gpu #for GPU-versoin tf
conda list => list all packages installed

10. 5 steps in the neural network model in Keras
probabilities = model.predict(test_X)
predictions = [float(np.round(x)) for x in probabilities]
accuracy = np.mean(predictions == Y)
model = Sequential()
model.add(Dense(5, input_dim=2))
model.add(Activation('relu'))
model.add(Dense(1))
model.add(Activation('sigmoid')
print(model.summary())
model.compile(optimizer=‘sgd', loss='mse', metrics=['accuracy'])
history = model.fit(X, y, batch_size=10, epochs=100)
loss, accuracy = model.evaluate(test_X, Y)

11. 神經網路 Neural Networks
• Basic building block for composition is a
perceptron (Rosenblatt c.1960)
• Linear classifier – vector of weights w and a
‘bias’ b
Output (binary)
11

12. Basic Concept
x1
x2
0.5
0.5
- 0.7
AND
0.7
x1
x2
-0.5
-0.5
OR

13. What’s Then/Next ?

14. What’s Then/Next ?
Turning large amounts of data into actionable
insights
14

15. 邊緣運算 (Edge Computing)
15
。
AI與物聯網趨勢並起將翻轉嵌入式設計思維

16. 邊緣運算: 嵌入式AI技術實現架構
Raspberry Pi
Intel NCS
Nvidia TX2
Development Kit
Computer Vision using
OpenCV
Deep Learning
DNN
Reinforcement
Learning
CNN RNN
GAN
Embedded Linux
or
16

17. Artificial Intelligence at the edge
on-board AI to process complex data without relying on network
connectivity. AI at the Edge is the future of industry, transforming
processes in manufacturing, industrial inspection, agriculture, general
robotics, security, and AI cities.
17
https://developer.nvidia.com/embedded-computing
Jetson TX2
終端裝置AI運算的實現

18. JETSON TX2 開發人員套件
18

19. Google Edge TPU
19
Add accelerated ML to your embedded device
Edge TPU Accelerator
Edge TPU Dev Board
https://aiyprojects.withgoogle.com/edge-tpu/

20. Load a pre-trained
model
on Raspberry Pi 3
PredictTest data
Intel NCS Edge Computing
Neural Compute Stick20

21. 21

22. Deep Learning on computer vision
Image Recognition
Object Detection
Segmentation
22

23. 更多其他深度學習的應用領域
23

24. AIoT軟體架構圖
http://blog.ittraining.com.tw/search/label/%E7%89%A9%E8%81%AF%E7%B6%B2
24

25. Example: AIoT軟體架構圖
25

26. Lab: Handwriting Digital Recognition
DNN實作分類問題-使用Keras (see demo code)
CNN實作圖像分類問題-使用Keras (see demo code)
26

27. 利用Deep Learning 進行Classification
27

28. MNIST data set
28

29. 建構一個DNN完成分類預測
29

30. 物件識別使用影像處理的流程
Capture
image
Object
Detection
(YoLo)
Image
Preprocessing
Image
Preprocessing
Object
Recognition

31. Demo: traffic_sign_detection
http://localhost:8888/tree/code/20181218_python/darkflow/tensorflow/models/research/object_detection/
traffic_sign_detection_V1.ipynb

32. Demo: traffic_sign_detection

33. Demo: traffic_sign_recognition
http://localhost:8888/notebooks/code/20181218_python/ML/Keras/cnn/Traffic%20Sign/traffic_sign_recognition_kerasdemo_20180714.ipynb

34. Thank you