This document summarizes Satyadhyan Chickerur's presentation on AI fundamentals and deep learning frameworks using IBM Power Systems. The presentation introduces neural network architectures like feedforward neural networks, recurrent neural networks, LSTMs and CNNs. It then summarizes four case studies applying these architectures: automatic detection of facial expressions using 3D modeling, an LSTM approach for lip reading Devanagari script, comparing change detection algorithms on multispectral imagery for classification, and combining RGB and depth images for indoor scene classification with deep learning. The document also briefly discusses machine learning versus deep learning and popular deep learning frameworks like TensorFlow, Caffe and IBM PowerAI.

1. AI Fundamentals and Deep learning
Frameworks using IBM Power Systems
Satyadhyan Chickerur, Ph.D
Prof, School of Computer Science and Engineering

2. Agenda
• Deep Learning in AI Landscape
• Neural Network Overview
• Neural Network Architectures(FFNN,RNN, LSTM,CNN)
• C1-Automatic detection of facial expressions and emotions
• C2-LSTM Based Lip Reading Approach for Devanagiri Script
• C3-Performance Analysis of Change Detection Algorithms On Multispectral Imagery
and Classification
• C4-Combining RGB and Depth images for Indoor Scene Classification using Deep
Learning

3. Deep Learning in the AI landscape
Ref :IBM PowerAI: Deep Learning Unleashed on IBM Power Systems Servers / RedBooks

4. Neural networks overview
Single Neuron

5. Neural Network Architectures
A)Feed Forward Neural Network
B)Recurrent Neural Network
C)LSTM
D)CNN

6. A) FeedForward Neural Nets
FeedForward Neural Network

7. B. Recurrent Neural network

8. C. LSTM
Ref : https://colah.github.io/posts/2015-08-Understanding-LSTMs/

9. D. CNN
Ref :https://medium.com/machine-learning-researcher/convlutional-neural-network-cnn-2fc4faa7bb63

10. Machine Learning Vs Deep Learning
Ref : https://www.smlease.com/entries/technology/machine-learning-vs-deep-learning-what-is-the-difference-between-ml-and-dl/

11. Machine Learning Vs Deep Learning
Ref :IBM PowerAI: Deep Learning Unleashed on IBM Power Systems Servers / RedBooks

12. ML WorkFlow —> DL WorkFlow
Ref : https://www.smlease.com/entries/technology/machine-learning-vs-deep-learning-what-is-the-difference-between-ml-and-dl/

13. C1- Automatic detection of facial expressions and emotions
Architecture Diagram

14. Automatic detection of facial expressions and emotions
Keypoint Extraction

15. Automatic detection of facial expressions and emotions
Data set - PCL / Kinect

16. Automatic detection of facial expressions and emotions
3D Model - matching and Expression Recogn

17. Automatic detection of facial expressions and emotions
Results

18. C2-LSTM Based Lip Reading Approach for Devanagiri Script
Architecture Diagram

19. LSTM Based Lip Reading Approach for Devanagiri Script
Keypoint Extraction

20. LSTM Based Lip Reading Approach for Devanagiri Script
Data Set
Paragraph for the Data Set

21. LSTM Based Lip Reading Approach for Devanagiri Script
Scaling - Two Approaches

22. LSTM Based Lip Reading Approach for Devanagiri Script
Overall Model

23. LSTM Based Lip Reading Approach for Devanagiri Script
Results - Word Recognition
Results - Sentence Recognition

24. C3- Performance Analysis of Change Detection Algorithms On
Multispectral Imagery and Classification
• Change detection - Selecting the two different images from the same region with
different frames of time, comparing those images, and detecting the areas where
changes have occurred.
• The various unsupervised change detection algorithms considered for our study are (1)
Principal Component Analysis (PCA), (2) PCA with K-means clustering, (3) Multivariate
Alteration Detection (MAD), (4) Iteratively Reweighted MAD (IRMAD).

25. Performance Analysis of Change Detection Algorithms On
Multispectral Imagery and Classification
Overall Model

26. Performance Analysis of Change Detection Algorithms On
Multispectral Imagery and Classification
Pine Data Set

27. Performance Analysis of Change Detection Algorithms On
Multispectral Imagery and Classification
Results

28. C4-Combining RGB and Depth images for Indoor Scene
Classification using Deep Learning
Overall Model

29. Combining RGB and Depth images for Indoor Scene
Classification using Deep Learning

30. Combining RGB and Depth images for Indoor Scene
Classification using Deep Learning
Data Set

31. Combining RGB and Depth images for Indoor Scene
Classification using Deep Learning
Results

32. Deep learning frameworks
source: https://aws.amazon.com/ko/machine-learning/amis/

33. IBM PowerAI
Ref : https://siliconangle.com/2017/05/10/ibm-beefs-powerai-software-accelerate-deep-learning-models/

34. Thank You