9 - Deep Learning 2019/2020

Deep Learning (DL) has attracted much interest in a wide range of applications such as image recognition, speech recognition and artificial intelligence, both from academia and industry. This lecture introduces the core elements of neural networks and deep learning, it comprises:
	
		
		(multilayer) perceptron, backpropagation, fully connected neural networks
		
		
		loss functions and optimization strategies
		
		
		convolutional neural networks (CNNs)
		
		
		activation functions
		
		
		regularization strategies
		
		
		common practices for training and evaluating neural networks
		
		
		visualization of networks and results
		
		
		common architectures, such as LeNet, Alexnet, VGG, GoogleNet
		
		
		recurrent neural networks (RNN, TBPTT, LSTM, GRU)
		
		
		deep reinforcement learning
		
		
		unsupervised learning (autoencoder, RBM, DBM, VAE)
		
		
		generative adversarial networks (GANs)
		
		
		weakly supervised learning
		
		
		applications of deep learning (segmentation, object detection, speech recognition, ...)
		
	

	The accompanying exercises will provide a deeper understanding of the workings and architecture of neural networks.

Education

Deep Learning (DL) has attracted much interest in a wide range of applications such as image recognition, speech recognition and artificial intelligence, both from academia and industry. This lecture introduces the core elements of neural networks and deep learning, it comprises: (multilayer) perceptron, backpropagation, fully connected neural networks loss functions and optimization strategies convolutional neural networks (CNNs) activation functions regularization strategies common practices for training and evaluating neural networks visualization of networks and results common architectures, such as LeNet, Alexnet, VGG, GoogleNet recurrent neural networks (RNN, TBPTT, LSTM, GRU) deep reinforcement learning unsupervised learning (autoencoder, RBM, DBM, VAE) generative adversarial networks (GANs) weakly supervised learning applications of deep learning (segmentation, object detection, speech recognition, ...) The accompanying exercises will provide a deeper understanding of the workings and architecture of neural networks.

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9 - Deep Learning 2019/2020

9 - Deep Learning 2019/2020