Deep Learning
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Deep Learning

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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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52 jagu
alates 13. aprill 2020
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Deep Learning - Introduction Part 2

Deep Learning - Introduction Part 2

Deep Learning - Introduction Part 2   This video introduces the topic of Deep Learning by showing several well-known examples from FAU.   Video References: Lex Fridman's Channel Car Scene  Mitotic Dataset    Further Reading A gentle Introduction to Deep Learning 
00:18:41
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EELMISED OSAD

2 - Deep Learning - Introduction Part 2

2 - Deep Learning - Introduction Part 2

00:18:41
JAGA
SALVESTA
2 - Deep Learning - Introduction Part 2 2020

2 - Deep Learning - Introduction Part 2 2020

Deep Learning - Introduction Part 2   This video introduces the topic of Deep Learning by showing several well-known examples from FAU.   Video References: Lex Fridman's Channel Car Scene  Mitotic Dataset    Further Reading A gentle Introduction to Deep Learni…
00:18:41
JAGA
SALVESTA
Deep Learning - Introduction Part 3

Deep Learning - Introduction Part 3

  Deep Learning - Introduction Part 3   This video introduces the topic of Deep Learning and discusses challenges and future directions.   Video References: Lex Fridman's Channel   Further Reading A gentle Introduction to Deep Learning   
00:08:48
JAGA
SALVESTA
3 - Deep Learning - Introduction Part 3

3 - Deep Learning - Introduction Part 3

00:08:48
JAGA
SALVESTA
3 - Deep Learning - Introduction Part 3 2020

3 - Deep Learning - Introduction Part 3 2020

  Deep Learning - Introduction Part 3   This video introduces the topic of Deep Learning and discusses challenges and future directions.   Video References: Lex Fridman's Channel   Further Reading A gentle Introduction to Deep Learning   
00:08:48
JAGA
SALVESTA
Deep Learning - Introduction Part 4

Deep Learning - Introduction Part 4

  Deep Learning - Introduction Part 4   This video introduces the topic of Deep Learning by defining the course's notation, introducing fundamental principles of Pattern Recognition, and a short review of the perceptron.   Video References: Lex Fridman's Channel   Further Reading A gentl…
00:14:48
JAGA
SALVESTA
4 - Deep Learning - Introduction Part 4

4 - Deep Learning - Introduction Part 4

00:14:48
JAGA
SALVESTA
4 - Deep Learning - Introduction Part 4 2020

4 - Deep Learning - Introduction Part 4 2020

  Deep Learning - Introduction Part 4   This video introduces the topic of Deep Learning by defining the course's notation, introducing fundamental principles of Pattern Recognition, and a short review of the perceptron.   Video References: Lex Fridman's Channel &nbs…
00:14:48
JAGA
SALVESTA
Deep Learning - Introduction Part 5

Deep Learning - Introduction Part 5

  Deep Learning - Introduction Part 5   This video introduces the topic of Deep Learning and presents the course's requirements, grading procedures, and summarises the first unit.   Video References: Lex Fridman's Channel   References [1] David Silver, Julian Schrittwieser, Karen Simonya…
00:06:29
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Vastutamatussäte: Sellele lehele manustatud taskuhäälingusaade ja kunstiteosed pärinevad Prof. Dr.-Ing. Andreas Maier-lt, mis on selle omaniku omand ja ei ole seotud ettevõttega Listen Notes, Inc.
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