|
Goodfellow, Bengio, Courville - «Deep Learning» (2016)
|
|
2
|
2619
|
April 2, 2024
|
|
Simple machine learning algorithms
|
|
0
|
732
|
May 24, 2019
|
|
Increasing accuracy, complexity and real-world impact
|
|
0
|
726
|
May 25, 2019
|
|
Increasing model sizes
|
|
0
|
637
|
May 25, 2019
|
|
The number of neurons in animals and artificial neural networks
|
|
1
|
2284
|
May 25, 2019
|
|
The number of connections per neuron in animals and artificial neural networks
|
|
1
|
1781
|
May 25, 2019
|
|
The deep learning's history
|
|
0
|
1463
|
May 25, 2019
|
|
Simple linear models: predecessors of modern deep learning
|
|
0
|
619
|
May 25, 2019
|
|
Increasing dataset sizes
|
|
0
|
2089
|
May 25, 2019
|
|
The third wave of neural networks research
|
|
0
|
876
|
May 25, 2019
|
|
What is «connectionism»?
|
|
0
|
517
|
May 25, 2019
|
|
Deep neural networks in the mid-1990s - mid-2000s
|
|
0
|
593
|
May 25, 2019
|
|
What is «distributed representation»?
|
|
0
|
619
|
May 25, 2019
|
|
Why has the neuroscience's role in deep learning been diminished?
|
|
1
|
901
|
May 25, 2019
|
|
What is the «computational neuroscience»?
|
|
0
|
575
|
May 25, 2019
|
|
What has neuroscience given to deep learning?
|
|
1
|
948
|
May 25, 2019
|
|
Limitations of linear models
|
|
0
|
586
|
May 25, 2019
|
|
Historical trends in deep learning
|
|
0
|
698
|
May 24, 2019
|
|
Relationships between deep learning, representation learning, machine learning, and artificial intelligence
|
|
0
|
2640
|
May 24, 2019
|
|
2 main ways of measuring the depth of a deep learning model
|
|
0
|
1796
|
May 24, 2019
|
|
How does deep learning solve the central problem in representation learning?
|
|
0
|
1077
|
May 24, 2019
|
|
What is a «feature»?
|
|
0
|
732
|
May 24, 2019
|
|
What is «disentangling factors of variation»?
|
|
0
|
483
|
May 24, 2019
|
|
What are «factors of variation»?
|
|
0
|
533
|
May 24, 2019
|
|
What is «representation learning»?
|
|
0
|
601
|
May 24, 2019
|
|
Сhoice of representation
|
|
0
|
723
|
May 24, 2019
|