Deep Learning - Segmentation and Object Detection Part 2

In this video, we discuss ideas on how to improve on image segmentation. In particular skip connections as used in the U-Net have been applied very successfully here. Also, we, look into other advanced methods such as stacked hourglasses, convolutional pose machines, and conditional random fields in combination with deep learning.

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>

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 2 In this video, we discuss ideas on how to improve on image segmentation. In particular skip connections as used in the U-Net have been applied very successfully here. Also, we, look into other advanced methods such as stacked hourglasses, convolutional pose machines, and conditional random fields in combination with deep learning. 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 Further Reading: A gentle Introduction to Deep Learning

Deep Learning - Segmentation and Object Detection Part 2

In this video, we discuss ideas on how to improve on image segmentation. In particular skip connections as used in the U-Net have been applied very successfully here. Also, we, look into other advanced methods such as stacked hourglasses, convolutional pose machines, and conditional random fields in combination with deep learning.

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>

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

Deep Learning - Segmentation and Object Detection Part 2

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;

Share Deep Learning - Segmentation and Object Detection Part 2

Sign up to save your podcasts

Deep Learning - Segmentation and Object Detection Part 2

Deep Learning - Segmentation and Object Detection Part 2

More shows like Deep Learning - Plain Version

Public Theology - Religion - Education. Interreligious Perspectives

Lineare Algebra I

Foundations of Quantum Mechanics

Die deutsche Königswahl (1125-1411)

Lectures on Quantum Theory (Elite Graduate Programme) 2015 -Measurements

Einführungsvorlesung Mittelalter

Lectures on the Geometric Anatomy of Theoretical Physics

Quantum Computing

Modern Optics 3: Quantum Optics