Sign up to save your podcasts
Or
Share Deep Learning
Share to email
Share to Facebook
Share to X
Share to email
Share to Facebook
Share to X
Or
Deep Learning
By Prof. Dr.-Ing. Andreas MaierEducation
Deep Learning episodes
- 23 min
- 18 min
- 20 min
- Deep Learning - Feedforward Networks Part 1Deep Learning - Feedforward Networks Part 1This 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 ChannelWatchMojo Game of Thromes QuotesDragon Ball SceneMorf's ChannelFurther ReadingA gentle Introduction to Deep Learning20 min
- 20 min
- 19 min
- 9 min
- 15 min
- 7 min
- Deep Learning - Introduction Part 2Deep Learning - Introduction Part 2This video introduces the topic of Deep Learning by showing several well-known examples from FAU.Video References:Lex Fridman's ChannelCar SceneMitotic DatasetFurther ReadingA gentle Introduction to Deep Learning19 min
About Deep Learning
From the publisher's feed
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.