Deep Learning - Feedforward Networks Part 1
	 
	This video introduces the topic of feedforward networks, universal approximation, and how to map a decision tree onto a neural network.
	 
	Video References:
	<a href="https://www.youtube.com/channel/UCSHZKyawb77ixDdsGog4iWA">Lex Fridman's Channel</a>
	<a href="https://www.youtube.com/watch?v=QXRrXO3czAI">WatchMojo</a> Game of Thromes Quotes
	<a href="https://www.youtube.com/watch?v=SiMHTK15Pik">Dragon Ball Scene</a>
	<a href="https://www.youtube.com/channel/UC1QugCUMNx9Jw2PQgnEne_A">Morf's Channel</a>
	 
	Further Reading
	<a href="https://www.sciencedirect.com/science/article/pii/S093938891830120X">A gentle Introduction to Deep Learning</a>

Deep Learning - Feedforward Networks Part 1 This video introduces the topic of feedforward networks, universal approximation, and how to map a decision tree onto a neural network. Video References: Lex Fridman's Channel WatchMojo Game of Thromes Quotes Dragon Ball Scene Morf's Channel Further Reading A gentle Introduction to Deep Learning

Deep Learning - Feedforward Networks&nbsp;Part 1
	&nbsp;
	This video introduces the topic of feedforward networks, universal approximation, and how to map a decision tree onto a neural network.
	&nbsp;
	Video References:
	<a href="https://www.youtube.com/channel/UCSHZKyawb77ixDdsGog4iWA" rel="noopener noreferrer">Lex Fridman's Channel</a>
	<a href="https://www.youtube.com/watch?v=QXRrXO3czAI" rel="noopener noreferrer">WatchMojo</a>&nbsp;Game of Thromes Quotes
	<a href="https://www.youtube.com/watch?v=SiMHTK15Pik" rel="noopener noreferrer">Dragon Ball Scene</a>
	<a href="https://www.youtube.com/channel/UC1QugCUMNx9Jw2PQgnEne_A" rel="noopener noreferrer">Morf's Channel</a>
	&nbsp;
	Further Reading
	<a href="https://www.sciencedirect.com/science/article/pii/S093938891830120X" rel="noopener noreferrer">A gentle Introduction to Deep Learning</a>

Deep Learning - Feedforward Networks Part 1

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.

Share Deep Learning - Feedforward Networks Part 1

Sign up to save your podcasts

Deep Learning - Feedforward Networks Part 1

Deep Learning - Feedforward Networks Part 1