Neural networks and deep learning

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Tijmen Blankenvoort, co-founder Scyfer BV, presentation at Artificial Intelligence Meetup 15-1-2014. Introduction into Neural Networks and Deep Learning.

Technologie Bildung

Neural Networks
and Deep Learning
Tijmen Blankevoort

Scyfer

Prof dr. Max Welling

Drs. Jorgen Sandig

Msc. Taco Cohen

Deep Learning
All purpose machine learning
Using Neural Networks:
- Using large amounts of data
- Learning very complex problems
- Automatically learning features
A new era of machine learning

Deep learning wins all competitions
- IJCNN 2011 Traffic Sign Recognition Competition
- ISBI 2012 Segmentation of neuronal structures in EM
stacks challenge
- ICDAR 2011 Chinese
handwriting recognition

Applications
A lot of state of the art systems use deep learning to some
extent:
- IBMs Watson: Jeopardy contest 2011
- Google’s self-driving car
- Google Glasses
- Facebook face recognition
- Facebook user modelling
Mostly image and sound recognition tasks (difficult)

Google Brain (2011)
- 10 million youtube/imagenet images
- 1 billion parameters
- 16.000 processors
- Largely unsupervised!
- 20.000 categories
- 15.8% accuracy

Bigger, better
Deep Learning:
- The scope of what
computers can learn has
greatly been increased
- Interaction with the real
world

Activation function

Sigmoid activation function

Neuron computer model

Perceptron - 1957 Rosenblatt

Linking neurons and training
- Initialize randomly
- Sequentially give it data.
- See what the difference is between
network output and actual output.
- Update the weights according to this error.
- End result: give a model input, and it
produces a proper output.
Quest for the weights. The weights are the
model!

The Perceptron (1958)

“A machine which senses, recognizes, remembers, and responds like the human mind”
“Remarkable machine… [was] capable of what amounts to thought” - The New Yorker

Criticism and downfall (1969)
- Perceptrons are painfully limited. They can not even learn a
simple XOR function!
- No feasible way of learning networks with multiple layers
- Interest in neural networks close to fully disappeared

Renewed interest (90’s)
- Learning multiple layers
- “Back propagation”
- Can theoretically learn any
function!
But…
Very slow and inefficient
- Machine learning attention
towards SVMs, random forests
etc.

Deep learing (2006)
- Quest: Mimic human brain representations
- Large networks
- Lots of data

Problem:
Simple back propagation fails
on large networks.

Deep learning (2006)
- Exactly same networks as
before, just BIGGER

- Combination of three factors:
- (Big data)
- Better algorithms
- Parallel computing (GPU)

Better algorithms

Restricted Boltzmann machine
Pre-training: Learn the representation by parts!
Very strong unsupervised learning
After pre-training, use back propagation

Parallel (GPU) power
- Every set of weights can be stored as a matrix (w_ij)
- GPUs are made to do common parallel problems fast!
- All similar calculations done at the same time, huge performance boost.
- CPU parallelizing

Future of Deep Learning
- Currently an explosion of developments
- Hessian-Free networks (2010)
- Long Short Term Memory (2011)
- Large Convolutional nets, max-pooling (2011)
- Nesterov’s Gradient Descent (2013)
- Currently state of the art but...
- No way of doing logical inference (extrapolation)
- No easy integration of abstract knowledge
- Hypothetic space bias might not conform with reality

When to apply Deep Learning
- Generally, vision and sound
recognition, but...
- Works great for any other problem too!
- A lot of data / features
- Don’t want to make your own features
- State of the art results

How to apply Deep Learning
Deep learning is very difficult!
- No easy plug and play software
- Far too many different networks/options/additions
- Mathematics and programming very challenging
- Research is fast paced
- Learning a network is both an art and a science
My advice:
Cooperation university <=> business

How to apply Deep Learning
- For most current business problems, no need for
expensive hardware. e.g. we use a laptop

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Effective data discovery is crucial for maintaining compliance and mitigating risks in today's rapidly evolving privacy landscape. However, traditional manual approaches often struggle to keep pace with the growing volume and complexity of data. Join us for an insightful webinar where industry leaders from TrustArc and Privya will share their expertise on leveraging AI-powered solutions to revolutionize data discovery. You'll learn how to: - Effortlessly maintain a comprehensive, up-to-date data inventory - Harness code scanning insights to gain complete visibility into data flows leveraging the advantages of code scanning over DB scanning - Simplify compliance by leveraging Privya's integration with TrustArc - Implement proven strategies to mitigate third-party risks Our panel of experts will discuss real-world case studies and share practical strategies for overcoming common data discovery challenges. They'll also explore the latest trends and innovations in AI-driven data management, and how these technologies can help organizations stay ahead of the curve in an ever-changing privacy landscape.

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Neural networks and deep learning

1. Neural Networks and Deep Learning Tijmen Blankevoort

2. Scyfer Prof dr. Max Welling Drs. Jorgen Sandig Msc. Taco Cohen

3. Deep Learning All purpose machine learning Using Neural Networks: - Using large amounts of data - Learning very complex problems - Automatically learning features A new era of machine learning

4. Deep learning wins all competitions - IJCNN 2011 Traffic Sign Recognition Competition - ISBI 2012 Segmentation of neuronal structures in EM stacks challenge - ICDAR 2011 Chinese handwriting recognition

5. Applications A lot of state of the art systems use deep learning to some extent: - IBMs Watson: Jeopardy contest 2011 - Google’s self-driving car - Google Glasses - Facebook face recognition - Facebook user modelling Mostly image and sound recognition tasks (difficult)

6. Google Brain (2011) - 10 million youtube/imagenet images - 1 billion parameters - 16.000 processors - Largely unsupervised! - 20.000 categories - 15.8% accuracy

7. Bigger, better Deep Learning: - The scope of what computers can learn has greatly been increased - Interaction with the real world

8. Biological Inspiration Neuron

9. Neuron computer model

10. Activation function Sigmoid activation function

11. Neuron computer model Perceptron - 1957 Rosenblatt

12. Easy functions with a neuron

13. Linking neurons and training - Initialize randomly - Sequentially give it data. - See what the difference is between network output and actual output. - Update the weights according to this error. - End result: give a model input, and it produces a proper output. Quest for the weights. The weights are the model!

14. The Perceptron (1958) “A machine which senses, recognizes, remembers, and responds like the human mind” “Remarkable machine… [was] capable of what amounts to thought” - The New Yorker

15. Criticism and downfall (1969) - Perceptrons are painfully limited. They can not even learn a simple XOR function! - No feasible way of learning networks with multiple layers - Interest in neural networks close to fully disappeared

16. Renewed interest (90’s) - Learning multiple layers - “Back propagation” - Can theoretically learn any function! But… Very slow and inefficient - Machine learning attention towards SVMs, random forests etc.

17. Deep learing (2006) - Quest: Mimic human brain representations - Large networks - Lots of data Problem: Simple back propagation fails on large networks.

18. Deep learning (2006) - Exactly same networks as before, just BIGGER - Combination of three factors: - (Big data) - Better algorithms - Parallel computing (GPU)

19. Better algorithms Restricted Boltzmann machine Pre-training: Learn the representation by parts! Very strong unsupervised learning After pre-training, use back propagation

20. Parallel (GPU) power - Every set of weights can be stored as a matrix (w_ij) - GPUs are made to do common parallel problems fast! - All similar calculations done at the same time, huge performance boost. - CPU parallelizing

21. Future of Deep Learning - Currently an explosion of developments - Hessian-Free networks (2010) - Long Short Term Memory (2011) - Large Convolutional nets, max-pooling (2011) - Nesterov’s Gradient Descent (2013) - Currently state of the art but... - No way of doing logical inference (extrapolation) - No easy integration of abstract knowledge - Hypothetic space bias might not conform with reality

22. When to apply Deep Learning - Generally, vision and sound recognition, but... - Works great for any other problem too! - A lot of data / features - Don’t want to make your own features - State of the art results

23. How to apply Deep Learning Deep learning is very difficult! - No easy plug and play software - Far too many different networks/options/additions - Mathematics and programming very challenging - Research is fast paced - Learning a network is both an art and a science My advice: Cooperation university <=> business

24. How to apply Deep Learning - For most current business problems, no need for expensive hardware. e.g. we use a laptop

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