This document provides an overview of deep learning and image recognition. It discusses topics like image classification, object detection, machine learning techniques including supervised and unsupervised learning. It also covers convolutional neural networks and commonly used deep learning software and datasets like MNIST, CIFAR, and ImageNet. The document aims to outline key concepts in machine learning, deep learning, and their applications to computer vision tasks.

1. 1
深度學習及影像辨識
(Deep Learning and Image Recognition)
fangkuoyu@gmail.com
12/26/2017

2. 圖像分類及圖像檢測
2
classification
detection
Source: https://github.com/kjw0612/awesome-deep-vision

3. 圖像分類及圖像檢測
3
Source: Hardware Architectures for Deep Neural Network (MIT) and
Convolutional Neural Networks for Visual Recognition (Stanford)

4. 機器學習/深度學習/軟體⽀支援
(machine learning/deep learning/software support)
圖像分類及圖像檢測
(classification and detection — case study)
其他圖像應⽤用
(other interesting applications)
內容⼤大綱
4
3
2
1

5. Machine Learning Landscape
5Source: http://www.techjini.com/blog/machine-learning/

6. Supervised Learning
6
Source: http://ogrisel.github.io/scikit-learn.org/sklearn-tutorial/auto_examples/tutorial/
plot_ML_flow_chart.html

7. Supervised Learning
7
Binary
Classification
Source: Real-World Machine Learning

8. Unsupervised Learning
8
Source: http://ogrisel.github.io/scikit-learn.org/sklearn-tutorial/auto_examples/tutorial/
plot_ML_flow_chart.html

9. Unsupervised Learning
9Source: http://adilmoujahid.com/posts/2016/06/introduction-deep-learning-python-caffe/
Cluster Analysis

10. Supervised v.s. Unsupervised
10
Problem Formulation ?!

11. Traditional v.s. Deep Learning
11
Source: http://ogrisel.github.io/scikit-learn.org/sklearn-tutorial/auto_examples/tutorial/
plot_ML_flow_chart.html

12. Convolutional Neural Network
12
Source: https://adeshpande3.github.io/adeshpande3.github.io/A-Beginner%27s-Guide-To-
Understanding-Convolutional-Neural-Networks/

13. Convolutional Neural Network
13
convolution
with
3x3 filter
max polling
with
2x2 pool
Source: https://cambridgespark.com/content/tutorials/convolutional-
neural-networks-with-keras/index.html

14. Data-Driven Approach
14
Source: http://ogrisel.github.io/scikit-learn.org/sklearn-tutorial/auto_examples/tutorial/
plot_ML_flow_chart.html

15. Image Dataset
15
Source: https://cambridgespark.com/content/tutorials/convolutional-
neural-networks-with-keras/index.html
MNIST CIFAR-10 / CIFAR-100

16. ImageNet Dataset
16Source: http://www.image-net.org
14,197,122 images
21,841 synsets

17. Deep Learning Software
17Source: https://www.svds.com/getting-started-deep-learning/

18. Deep Learning Software
18Source: https://mobile.twitter.com/fchollet/status/871089784898310144

19. 機器學習/深度學習/軟體⽀支援
(machine learning/deep learning/software support)
圖像分類及圖像檢測
(classification and detection — case study)
其他圖像應⽤用
(other interesting applications)
內容⼤大綱
19
3
2
1