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Introduction to
Deep Learning
(c) Oleg Mygryn 2016
Neuron Model
1899 - Discovered. Santiago Ramón
Neuron
Input

1
Input

2
Input

3
weight 2weight 1 weight 3
Activation Function
Output
1958 - Perceptron. Frank Rosenblatt
1984 - Backpropagation optimization
2010 - Recurrent and Deep FF nets
2012+ ….
ImageNet Challenge
150,000 images and 1000 object classes
top 5 suggestions, error rate, %
Deep Learning and last 10 years
- AlexNet (2012) exploded industry. 5 layers
- ZF Net (2013) - 5 layers improved AlexNet
- GoogLeNet (2015) - 22 layers
- VGG Net (2014) - Oxford 19, layers
In 2016:
- NVIDIA DGX-1 system. ~170TFlops!!!
- Intel® Xeon Phi™ 7210. ~ 3 TFlops
Top AI scientists
Geoffrey E. Hinton
University of Toronto,
AlexNet curator,
researcher
Andrew NgYann LeCun
Facebook,
AI research group,
working on AI
since 1998
Chief Scientist of Baidu,
Co-Founder Coursera,
Professor at Stanford University
Modeling Neuron
Human body:
~ 86 billion neurones
~ 100 trillons synapses
Activation functions:
- sigmoid;
- tanh;
- ReLU;
Layer-wise organization
Most networks are fully-connected
Not counting Input layer (3-layered on picture)
Output layer - no activation function
4 + 4 + 1 = 9 neurons
[3 x 4] + [4 x 4] + [4 x 1]
= 12 + 16 + 4 = 32 weights
4 + 4 + 1 = 9 biases
∑ = 41 learnable params
Modern NN ~100 million parameters with ~10-20 layers
Example: Visual Geometry Group Network (Oxford) have 19 layers and 138 Millions parameters to learn
Rectifier (ReLU)
TanH
Sigmoid
Binary
Activation Functions
ReLU Rocks !
ReLUs (solid line) reaches a 25% training error rate on CIFAR-10 six times faster
than an equivalent network with tanh neurons (dashed line)
by Alex Krizhevsky
TanH example
Convolutional Neural Networks (CNN)
CNN Example 1
CNN Example 2
https://github.com/rasbt/python-machine-learning-book
Training network. Backpropagation
AlexNet. Image recognition samples
https://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf
AlexNet (2012): Illustration of the Architecture
“Train time on GTX 580 3GB GPUs 5-6 days. All of our experiments suggest that our results can be improved
simply by waiting for faster GPUs and bigger datasets to become available” (c) Alex Krizhevsky
GoogleNet (2015): Inception
9 Inception modules
in the whole architecture,
with over 100 layers in total
GoogleNet (2015): Inception
9 Inception modules
in the whole architecture,
with over 100 layers in total
http://www.deepdrumpf2016.com/
@DeepDrumpf
Deconvolution
Deconvolution
@ Google
tensorflow.org launched in Nov 2015.
- most popular ML library
- GitHub: 35,000 stars 15,000 forks
- 350 contributors
TensorFlow
- Python API
- C++ API (poorly documented)
- Java API ??? (TBA in 201X)
API
Features
- CPU or multiple CPU, GPU or multiple GPU;
- Async computation with lazy loading of execution graph;
- Many of algorithm have already implemented;
- TensorBoard: graph execution visualisation + debugging;
import tensorflow as tf
hello = tf.constant('Hello, TensorWorld!')
sess = tf.Session()
print sess.run(hello)
Thank YOU
1. www.cs231n.github.io
2. www.tensorflow.org
3. www.playground.tensorflow.org
4. www.yann.lecun.com
5. www.kdnuggets.com
6. www.devblogs.nvidia.com

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