Simple machine learning algorithms
|
|
0
|
258
|
May 24, 2019
|
Increasing accuracy, complexity and real-world impact
|
|
0
|
252
|
May 25, 2019
|
Goodfellow, Bengio, Courville - «Deep Learning» (2016)
|
|
1
|
800
|
May 24, 2019
|
Increasing model sizes
|
|
0
|
215
|
May 25, 2019
|
The number of neurons in animals and artificial neural networks
|
|
1
|
526
|
May 25, 2019
|
The number of connections per neuron in animals and artificial neural networks
|
|
1
|
467
|
May 25, 2019
|
The deep learning's history
|
|
0
|
472
|
May 25, 2019
|
Simple linear models: predecessors of modern deep learning
|
|
0
|
204
|
May 25, 2019
|
Increasing dataset sizes
|
|
0
|
617
|
May 25, 2019
|
The third wave of neural networks research
|
|
0
|
300
|
May 25, 2019
|
What is «connectionism»?
|
|
0
|
178
|
May 25, 2019
|
Deep neural networks in the mid-1990s - mid-2000s
|
|
0
|
202
|
May 25, 2019
|
What is «distributed representation»?
|
|
0
|
205
|
May 25, 2019
|
Why has the neuroscience's role in deep learning been diminished?
|
|
1
|
345
|
May 25, 2019
|
What is the «computational neuroscience»?
|
|
0
|
232
|
May 25, 2019
|
What has neuroscience given to deep learning?
|
|
1
|
361
|
May 25, 2019
|
Limitations of linear models
|
|
0
|
204
|
May 25, 2019
|
Historical trends in deep learning
|
|
0
|
192
|
May 24, 2019
|
Relationships between deep learning, representation learning, machine learning, and artificial intelligence
|
|
0
|
759
|
May 24, 2019
|
2 main ways of measuring the depth of a deep learning model
|
|
0
|
351
|
May 24, 2019
|
How does deep learning solve the central problem in representation learning?
|
|
0
|
333
|
May 24, 2019
|
What is a «feature»?
|
|
0
|
303
|
May 24, 2019
|
What is «disentangling factors of variation»?
|
|
0
|
174
|
May 24, 2019
|
What are «factors of variation»?
|
|
0
|
174
|
May 24, 2019
|
What is «representation learning»?
|
|
0
|
204
|
May 24, 2019
|
Сhoice of representation
|
|
0
|
198
|
May 24, 2019
|