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.

1. Deep Learning
meetup
Protobuf/Lua (Caffe/Torch)
Python (TensorFlow)
Java (DeepLearning4J)

2. Programming vs ML

3. Stage of AI

4. Deep Learning history

5. ML algorithms

7. Training DL model

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