Deep Learning

• Neural Networks
• Multiple layers
• Fed with lots of Data

History

• 1980+ : Lots of enthusiasm for NNs
• 1995+ : Disillusionment = A.I. Winter (v2+)
• 2005+ : Stepwise improvement : Depth
• 2010+ : GPU revolution : Data

Who is involved

• Google - Hinton (Toronto)
• Facebook - LeCun (NYC)
• Baidu - Ng (Stanford)
• ... Apple (acquisitions), etc
• Universities, eg: Montreal (Bengio)

Deep Learning Now

in production in 2016

• Speech recognition
• Language translation
• Vision :
• • Object recognition
  • Automatic captioning
• Reinforcement Learning

Speech Recognition

Android feature since Jellybean (v4.3, 2012) using Cloud

Trained in ~5 days on 800 machine cluster

Embedded in phone since Android Lollipop (v5.0, 2014)

Translation

Google's Deep Models are on the phone

"Use your camera to translate text instantly in 26 languages"

Translations for typed text in 90 languages

House Numbers

Google Street-View (and ReCaptchas)

Better than human

ImageNet Results

(now human competitive on ImageNet)

Captioning Images

Some good, some not-so-good

Reinforcement Learning

Google's DeepMind purchase

Learn to play games from the pixels alone

Better than humans 2 hours after switching on

Reinforcement Learning

Google DeepMind's AlphaGo

Learn to play Go from (mostly) self-play

Basic Foundation

• Same as original Neural Networks in 1980s/1990s
• Simple mathematical units ...
• ... combine to compute a complex function

Single "Neuron"

Change weights to change output function

Multi-Layer

Layers of neurons combine and
can form more complex functions

Supervised Learning

• while not done :
• • Pick a training case (x → target_y)
  • Evaluate output_y from the x
  • Modify the weights so that output_y is closer to target_y for that x

Gradient Descent

Follow the gradient of the error
vs the connection weights

Training a Neural Network

• Time to play with :
• • Layers of different widths
  • Layers of different depths
• "Stochastic Gradient Descent" (SGD)

Workshop : SGD (local=BEST)

• • Go to the Javascript Painting Example

Workshop : SGD (online)

• • Go to : http://ConvNetJS.com/
  • Look for : "Image 'painting'"

Simple Network

Wider Network

Two-Ply Network

Deep Network and Time

What's Going On Inside?

• Time to look at :
• • Input features
  • What each neuron is learning
  • How the training converges

Workshop : Internals

• • Go to the Javascript Example : TensorFlow

(or search online for TensorFlow Playground)

TensorFlow Playground

Things to Do

• Investigate :
• • Minimal set of features
  • Minimal # of layers
  • Minimal widths
  • Effect of going less-minimal...

Image Classification

• ImageNet Competition
• over 15 million labeled high-resolution images...
• ... in over 22,000 categories

Convolution Neural Networks

• Pixels in an images are 'organised' :
• • Up/down left/right
  • Translational invariance
• Can apply a 'convolutional filter'
• Use same parameters over whole image

CNN Filter

Workshop : VirtualBox

• Import Appliance 'deep-learning ... .OVA'
• Start the Virtual Machine...

Workshop : Jupyter

• On your 'host' machine
• Go to http://localhost:8080/

Other VM Features

• There is no need to ssh - it should Just Work
• But if you want to have a poke around...
• From your 'host' machine :

ssh -p 8282 user@localhost     
# password=password
   

• or have a look at the code on GitHub...

Complex Network Example

GoogLeNet (2014)

3-ImageNet-googlenet

Play with a ~2014 pre-trained network

Need for Speed

... need for GPU programmers

Quick Overview of VM

Quick Overview of VM

Coming up in Part 2

Hands on ...

• Building networks using a Framework
• Doing one (or two) more advanced examples

8-Anomaly-Detection

MNIST again...

5-Commerce

Re-purpose a pretrained network

... Even More Complex Network

Google Inception-v3 (2015)

4-ImageNet-inception-v3

Play with a ~2015 pre-trained network

Abusing Pre-Built Networks

• Visual features can also be used generatively
• Alter images to maximise network response
• ...
• Art?

"Deep Dreams"

Careful! : Some images cannot be un-seen...

6-Visual-Art

Style-Transfer an artist onto your photos

Language Processing?

Variable-length input doesn't "fit"

• Apply a network iteratively over input
• Internal state carried forward step-wise
• Everything is still differentiable

Recurrent Neural Networks

A Long Short-Term Memory (LSTM) Unit

8-Natural-Language

Still a work-in-progress (training takes too long)

Poetry : Epoch 1

JDa&g#sdWI&MKW^gE)I}<UNK>f;6g)^5*|dXdBw6m\2&XcXVy\ph8G<gAM&>e4+mv5}OX8G*Yw9&n3XW{h@&T\Fk%BPMMI
OV&*C_] ._f$v4I~$@Z^&[2
mOVe`4W)"L-KClkO]wu]\$LCNadyo$h;>$jV7X$XK#4,T(y"sa6W0LWf\'_{\#XD]p%ck[;O`!Px\#E>/Or(.YZ|a]2}q|@a9.g3nV,U^qM	$+:nlk0sd;V-Z&;7Y@Z "l-7P^C
"xBF~~{n} n\ Pcbc9f?=y)FIc1h5kvjIi
C<UNK>s	DWJr_$ZQtu"BTYm'|SMj-]Z<Vqj*.lh%IYW|q.GK:eNI"r>833?+RuUsOj_)a{\T}gH.zZR^(daC3mg5P0iFi]bqGo4?T|\>0_H&g889voTh=~)^DDRYND46z1J]x;<U>>%eNIRckL)N8n<UNK>n3i)+Ln8
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@'HW.;I5)C.*%}<jcNLN+Z__RWoryOb#
/`r
   

Poetry : Epoch 100

Som the riscele his nreing the timest stordor hep pIs dach suedests her, so for farmauteds?
By arnouy ig wore
Thou hoasul dove he five grom ays he bare as bleen,
The seend,
And, an neeer,
Whis with the rauk with, for be collenss ore his son froven faredure:
Then andy bround'd the CowE nom shmlls everom thoy men ellone per in the lave ofpen the way ghiind, thour eyes in is ple gull heart sind, I I wild,
Frreasuce anspeve, wrom fant beiver, not the afan
And in thou' histwish a it wheme-tis lating ble the liveculd;
Noorroint he fhallought, othelts.
   

Poetry : Epoch 1000

AWhis grook my glass' to his sweet,
Bub my fears liken?
And of live every in seedher;
A Lood stall,
But tare tought than thencer sud earth,
Use'st bee sechion,
For all exprit' are a daud in heaven doth her infook perust the fork the tent.

For maud,
The pittent gover
This and rimp,
Who new
  
Thoir oldes and did hards, cound.
   

Plays : Epoch 338

Larger network...

DEDENIUS	Why shoulmeying to to wife,
	And thou say: and wall you teading for
	that struke you down as sweet one.
	With be more bornow, bly unjout on the account:
	I duked you did four conlian unfortuned drausing-
	to sicgia stranss, or not sleepplins his arms
	Gentlemen? as write lord; gave sold.

AENEMUUNS	Met that will knop unhian, where ever have
	of the keep his jangst?icks he I love hide,
	Jach heard which offen, sir!'

	[Exit PATIIUS, MARGARUS arr	[Enter CLOTHUR]
   

Reinforcement Learning

• Learning to choose actions ...
• ... which cause environment to change

Google DeepMind's AlphaGo

7-Reinforcement-Learning

Deep Reinforcement Learning for Bubble Breaker

Frameworks

• Want to express networks at a higher level
• Map network operations onto cores
• Most common frameworks :
• • Caffe - C++ ~ Berkeley
  • Torch - lua ~ Facebook/Twitter
  • Theano - Python ~ Montreal Lab
  • TensorFlow - C++ ~ Google

Theano

• Optimised Numerical Computation in Python
• Computation is described in Python code :
• • Theano operates on expression tree itself
  • Optimizes the tree for operations it knows
  • Makes use of numpy and BLAS
  • Also writes C/C++ or CUDA (or OpenCL)

0-TheanoBasics

Use the 'play' button to walk through the workbook

"Hello World" → MNIST

• Nice dataset from the late 1980s
• Training set of 50,000 28x28 images
• Now end-of-life as a useful benchmark

Simple Network

... around 2-3% error rate on the test set

1-MNIST-MLP

We using lasagne as an additional layer on top of Theano

2-MNIST-CNN

Three filter layers - nice visual interpretation

Anomaly Detection

How do we detect outliers?

• Learning what 'good' data looks like
• See whether new data looks the same
• ... if it doesn't : complain

Autoencoders

• Since we only have one 'label'
• ... train a network to reproduce its input
• Learning compressed representation
• ... is a biproduct

8-Anomaly-Detection

Using an Autoencoder

Network Picture

Using Pre-Built Networks

• Features learned for ImageNet are 'useful'
• Apply same network to 'featurized' new images
• Learn mapping from features to new classes
• Only have to train a single layer!

5-Commerce

Re-purpose a pretrained network

Network Picture

Fancier Tricks

Differentiable → Training : Even crazy stuff works!

Image Labelling

We have the components now

Image Labels