Learn the fundamental concepts and practical applications of neural networks, starting from biological neural network foundations through to advanced architectures like Deep Convolutional Neural Networks and Generative Adversarial Networks. Master various neural network models including Perceptrons, Back Propagation, RBF, Adaline, Hopfield, Elman, AdaBoost, and SOFM networks while exploring their theoretical underpinnings and real-world implementations. Progress from basic neuron models to sophisticated deep learning architectures in this comprehensive course from Chang'an University that bridges the gap between biological inspiration and artificial neural network applications.
Overview
Syllabus
- Chapter 1 Theoretical basis of biological neural network
- Chapter 2 Review of artificial neural network
- Chapter 3 Neuron model
- 3.1 Neuron model
- Chapter 4 Perceptrons
- Chapter 5 Back propagation neural network
- Chapter 6 RBF neural network
- Chapter 7 Adaline neural network
- Chapter 8 Hopfield neural network
- Chapter 9 Deep Convolutional Neural Network
- Chapter 10 Generative adversarial networks
- Chapter 11 Elman neural network
- Chapter 12 AdaBoost neural network
- Chapter 13 SOFM neural network
- Final Exam(期末考试)
Taught by
Wen Changbao, Ru Feng, Li Yanming, Quan Si, and Liu Youyao
