Goodfellow, Bengio, Courville - «Deep Learning» (2016)
|
|
2
|
2597
|
April 2, 2024
|
Simple machine learning algorithms
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|
0
|
729
|
May 24, 2019
|
Increasing accuracy, complexity and real-world impact
|
|
0
|
721
|
May 25, 2019
|
Increasing model sizes
|
|
0
|
634
|
May 25, 2019
|
The number of neurons in animals and artificial neural networks
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|
1
|
2275
|
May 25, 2019
|
The number of connections per neuron in animals and artificial neural networks
|
|
1
|
1773
|
May 25, 2019
|
The deep learning's history
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|
0
|
1444
|
May 25, 2019
|
Simple linear models: predecessors of modern deep learning
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|
0
|
615
|
May 25, 2019
|
Increasing dataset sizes
|
|
0
|
2077
|
May 25, 2019
|
The third wave of neural networks research
|
|
0
|
868
|
May 25, 2019
|
What is «connectionism»?
|
|
0
|
515
|
May 25, 2019
|
Deep neural networks in the mid-1990s - mid-2000s
|
|
0
|
586
|
May 25, 2019
|
What is «distributed representation»?
|
|
0
|
617
|
May 25, 2019
|
Why has the neuroscience's role in deep learning been diminished?
|
|
1
|
900
|
May 25, 2019
|
What is the «computational neuroscience»?
|
|
0
|
573
|
May 25, 2019
|
What has neuroscience given to deep learning?
|
|
1
|
945
|
May 25, 2019
|
Limitations of linear models
|
|
0
|
584
|
May 25, 2019
|
Historical trends in deep learning
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|
0
|
696
|
May 24, 2019
|
Relationships between deep learning, representation learning, machine learning, and artificial intelligence
|
|
0
|
2622
|
May 24, 2019
|
2 main ways of measuring the depth of a deep learning model
|
|
0
|
1790
|
May 24, 2019
|
How does deep learning solve the central problem in representation learning?
|
|
0
|
1072
|
May 24, 2019
|
What is a «feature»?
|
|
0
|
730
|
May 24, 2019
|
What is «disentangling factors of variation»?
|
|
0
|
481
|
May 24, 2019
|
What are «factors of variation»?
|
|
0
|
530
|
May 24, 2019
|
What is «representation learning»?
|
|
0
|
596
|
May 24, 2019
|
Сhoice of representation
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|
0
|
719
|
May 24, 2019
|