Deep Learning meetup
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The document discusses deep learning including popular programming languages for deep learning like Protobuf/Lua, Python, and Java. It covers convolutional neural networks and how they process input images through convolution layers and activation maps to reduce their size. Examples of famous deep learning models are provided from 1998 to the present like LeNet, AlexNet, VGGNet, and ResNet. Resources for deep learning papers, demos, and preparing for the future of AI are also listed.
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![ConvNets
32
32
3
Convolution Layer
activation maps
6
28
For example, if we had 6 5x5 filters, we’ll get 6 separate activation maps:
We processed [32x32x3] volume into [28x28x6] volume.
Q: how many parameters are used ?
A: (5*5*3)*6 = 450 parameters, (5*5*3)*(28*28*6) = ~350K multiplies
28](https://image.slidesharecdn.com/dlstartit-161028143207/85/Deep-Learning-meetup-9-320.jpg)
Deep Learning meetup
- 1. Deep Learning meetup Protobuf/Lua (Caffe/Torch) Python (TensorFlow) Java (DeepLearning4J)
- 3. Stage of AI
- 8. ConvNets 32 32 3 Convolution Layer 32x32x3 image 5x5x3 filter convolve (slide) over all spatial locations activation map 1 28 28
- 9. ConvNets 32 32 3 Convolution Layer activation maps 6 28 For example, if we had 6 5x5 filters, we’ll get 6 separate activation maps: We processed [32x32x3] volume into [28x28x6] volume. Q: how many parameters are used ? A: (5*5*3)*6 = 450 parameters, (5*5*3)*(28*28*6) = ~350K multiplies 28
- 11. ● InputLayer ● ConvLayer ● ReLuLayer ● PoolLayer ● FullyConnLayer ● SoftMaxLayer
- 12. Models ● LeNet - 1998 ● AlexNet - ILSVRC 2012 ● ZFNet - ILSVRC 2013 ● GoogLeNet - ILSVRC 2014 ● VGGNet - ILSVRC 2014 ● ResNet - ILSVRC 2015 ● ILSVRC 2016
- 13. Links ● Dl4j-examples ● Deep Learning Papers ● ConvNetJS CIFAR-10 demo ● NVIDIA DIGITS ● Darknet ● Preparing for the Future of AI ● Learning for Self-Driving Cars