Deep Learning - Segmentation and Object Detection Part 4

In this video, we look at some ideas on how to perform object detection really quickly. This leads to single shot detectors of which YOLO is one of the most popular ones. If you are in need of multi-scale object detection, Retina-Net is a popular choice.

Additional References 
<a href="https://arxiv.org/abs/1809.10486">nnU-Net: Self-adapting Framework for U-Net-Based Medical Image Segmentation</a> 
<a href="https://arxiv.org/abs/1803.08608">X-ray-transform Invariant Anatomical Landmark Detection for Pelvic Trauma Surgery</a> 
<a href="https://opus4.kobv.de/opus4-fau/frontdoor/index/index/docId/13755">Retina-net Figure by Marc Aubreville</a>

Further Reading: 
<a href="https://www.sciencedirect.com/science/article/pii/S093938891830120X">A gentle Introduction to Deep Learning</a>

Deep Learning - Segmentation and Object Detection Part 4 In this video, we look at some ideas on how to perform object detection really quickly. This leads to single shot detectors of which YOLO is one of the most popular ones. If you are in need of multi-scale object detection, Retina-Net is a popular choice. Additional References nnU-Net: Self-adapting Framework for U-Net-Based Medical Image Segmentation X-ray-transform Invariant Anatomical Landmark Detection for Pelvic Trauma Surgery Retina-net Figure by Marc Aubreville Further Reading: A gentle Introduction to Deep Learning

Deep Learning - Segmentation and Object Detection Part 4

In this video, we look at some ideas on how to perform object detection really quickly. This leads to single shot detectors of which YOLO is one of the most popular ones. If you are in need of multi-scale object detection, Retina-Net is a popular choice.

Additional References 
<a href="https://arxiv.org/abs/1809.10486" rel="noopener noreferrer">nnU-Net: Self-adapting Framework for U-Net-Based Medical Image Segmentation</a> 
<a href="https://arxiv.org/abs/1803.08608" rel="noopener noreferrer">X-ray-transform Invariant Anatomical Landmark Detection for Pelvic Trauma Surgery</a> 
<a href="https://opus4.kobv.de/opus4-fau/frontdoor/index/index/docId/13755" rel="noopener noreferrer">Retina-net Figure by Marc Aubreville</a>

Further Reading: 
<a href="https://www.sciencedirect.com/science/article/pii/S093938891830120X" rel="noopener noreferrer">A gentle Introduction to Deep Learning</a>

&nbsp;

Deep Learning - Segmentation and Object Detection Part 4

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:
	<ul>
		<li>
		(multilayer) perceptron, backpropagation, fully connected neural networks
		</li>
		<li>
		loss functions and optimization strategies
		</li>
		<li>
		convolutional neural networks (CNNs)
		</li>
		<li>
		activation functions
		</li>
		<li>
		regularization strategies
		</li>
		<li>
		common practices for training and evaluating neural networks
		</li>
		<li>
		visualization of networks and results
		</li>
		<li>
		common architectures, such as LeNet, Alexnet, VGG, GoogleNet
		</li>
		<li>
		recurrent neural networks (RNN, TBPTT, LSTM, GRU)
		</li>
		<li>
		deep reinforcement learning
		</li>
		<li>
		unsupervised learning (autoencoder, RBM, DBM, VAE)
		</li>
		<li>
		generative adversarial networks (GANs)
		</li>
		<li>
		weakly supervised learning
		</li>
		<li>
		applications of deep learning (segmentation, object detection, speech recognition, ...)
		</li>
	</ul>

	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.

&nbsp;


	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:
	<ul>
		<li>
		(multilayer) perceptron, backpropagation, fully connected neural networks
		</li>
		<li>
		loss functions and optimization strategies
		</li>
		<li>
		convolutional neural networks (CNNs)
		</li>
		<li>
		activation functions
		</li>
		<li>
		regularization strategies
		</li>
		<li>
		common practices for training and evaluating neural networks
		</li>
		<li>
		visualization of networks and results
		</li>
		<li>
		common architectures, such as LeNet, Alexnet, VGG, GoogleNet
		</li>
		<li>
		recurrent neural networks (RNN, TBPTT, LSTM, GRU)
		</li>
		<li>
		deep reinforcement learning
		</li>
		<li>
		unsupervised learning (autoencoder, RBM, DBM, VAE)
		</li>
		<li>
		generative adversarial networks (GANs)
		</li>
		<li>
		weakly supervised learning
		</li>
		<li>
		applications of deep learning (segmentation, object detection, speech recognition, ...)
		</li>
	</ul>

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


&nbsp;

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