NNDL

06046003 NEURAL NETWORK AND DEEP LEARNING

In this class, we study Neural networks and Deep learning being currently the most interesting in artificial intelligence works. By the subtopics consisting, the overview of artificial intelligence and machine learning, Neural network, the usage NN for recognition, training NN with a random walk, the back-propagation NN, evolutionary computation, improvement BNN with GA, a multi-layer hidden node in NN, the improvement MLNN with GA, and deep learning.

Schedule

NO.	DATE	TOPIC	DOWNLOAD
1		Chapter00 Course description Chapter01 Basic R-Programming	Chapter00 Chapter01 Metrial01
2		Chapter01 Basic R-programming
2		Chapter02 Perceptron	Chapter02 Metrial02
3		Chapter03 Feed Forward Neural Network	Chapter03
5		Chapter04 Multilayer perceptron	Chapter04
6		Chapter05: Feed Forward Neural Network	Chapter05 Metrial05
7		Chapter06: Applied FFNN	Chapter06 Metrial06
8		Chapter07: Recurrent Neural Network	Chapter07 Metrial07
9		Midterm Examination Week
10		Chapter08: Apply RNN	Chapter08 Metrial08
12		Chapter09: GRU, Long Short-Term Memory	Chapter09 Metrial09
13		Chapter10: Convolutional Neural Network	Chapter10 Metrial10
14		Chapter11: Apply Evolutionary Computation for solving NN problems	Chapter11
15
16
17
18

Score

6-Quiz: 70%
Final: 20%
Project: 10%

References

Wei Di, Anurag Bhardwaj, Jianing Wei, "Deep Learning Essentials", Packt Publishing, 2018

Library