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Neural Network Course

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Computer Science Faculty of Shahid Beheshti University. Winter 2023 {: .fs-6 .fw-300 }

The goal of our course is to provide students with a comprehensive understanding of the principles and techniques associated with neural networks, as well as the ability to apply these concepts to solve real-world problems. Our course will cover the fundamentals of neural networks, from the basics of training and backpropagation to more advanced topics such as convolutional neural networks, recurrent neural networks, and deep reinforcement learning. By the end of the course, students will be equipped with the knowledge and skills needed to apply neural networks to their own research and applications.

Lectures

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Week	Topic
1 - 4	Introduction to Neural Networks	History
		Biological intiuation
		Perceptron
		MLP
		Backpropagation
5 - 7	Enhancing Neural Networks	Optimization
		Weight Initialization
		Activation Functions
		Learning Rate Scheduling
		Regularization
8 - 11	Convolutional Neural Networks	Convolution
		Basic Concepts
		Popular Architectures
		Applications
12 - 14	Recurrent Neural Networks	Sequential Modeling
		Vanilla RNN
		BTT
		LSTM & GRU
15 - 16	Advanced Topics	Autoencoders
		Variational Autoencoders
		Image Captioning
		Deep Reinforcement Learning