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Cutting edge of Machine Learning
1.
Machine Learning The Cutting
Edge Sergii Shelpuk Director, Data Science SoftServe, Inc. sshel@softserveinc.com
2.
Classification Problem Recognize what
is a bike and what is a moon
3.
Classification Problem Classifier ©A. Ng
4.
Classification Problem pixel intensity
5.
Classification Problem Raw data
does not represent the picture well. You need some smart features contains wheels contains seas
6.
Feature Extraction Classifier Featureextractor ©A. Ng
7.
Feature Extraction Can we
do better?
8.
Neural Networks a a a a a a a a a a a a a a features bike moon
9.
Neural Networks
10.
Neural Networks aX a0 a1 a2 w0 w1 w2 Activation function: aX
= f(a0, a1, a2, w0, w1, w2) Example (logistic): aX = 1 / (1 + e-(a0*w0+a1*w1+a2*w2))
11.
Autoencoder
12.
Autoencoder © H. Lee
et al.
13.
Autoencoder © Q Le
et al.
14.
Deep Learning Neural
Network Pre-trained as Autoencoder Typical classification neural network Moon
15.
Deep Learning Neural
NetworkVideoText/NLPImages ©A. Ng
16.
Deep Learning Neural
Network Hints and Tips Using unlabeled data Avoiding overfitting Computational efficiency
17.
Using Unlabeled Data wheels handlebar
18.
Avoiding Overfitting Sparsity constraint
limits variance of autoencoder
19.
Avoiding Overfitting Dropout ensures
generalization of the neural network
20.
Computational Efficiency Thousands
of cores Base Clock: 300-900 MHz Memory: 2-6 Gb Performance: up to 3.5 Tflops Instruction-level parallelism Shared memory Up to 4 devices in cluster GPU computing provides cheapest computational power
21.
Feature Learning: MNIST Data: Features:
22.
Feature Learning: Galaxy
Zoo Data: Features:
23.
Thank you!
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