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Louis Monier
@louis_monier
https://www.linkedin.com/in/louismonier
A Taste of Deep Learning
You can do it !
Gregory Renard
@redo
https://www.linkedin.com/in/gregoryrenard
Class 0 - Q1 - 2016
The Big Bet
Credit: Google Trends
Why we are Bullish about the Future
...100 years...
...10 years...
???
...100 years?...
Projections
McKinsey projects $50T by 2025
- Knowledge work: big data, smart assistants, marketing, sports…
- Finance, health care, genomics, law, insurance...
- IoT: smart appliances, sensors, cameras, wearables
- Robots, autonomous vehicles, drones, manufacturing
… so VCs are pouring money into Deep Learning companies.
A Brief History of
Artificial Intelligence
How to define Artificial Intelligence?
Easy for People (intelligence)
Vision, speech, understanding language
Planning, common sense
Having a conversation
Reading emotions
Translating Russian to English
Playing Chess, Go
Proving math theorems
Easy for Machines (stamina)
Multiplying huge matrices
Searching large databases
Sorting a trillion records
Finding a path in a huge maze
Hard for People and Machines
Predicting the weather
Predicting the winning lottery ticket
1770
The Mechanical Turk
The “Turing Test”1950
1956Dartmouth Conference
Checkers-playing program
“Magic”
1957 Perceptrons, Frank Rosenblatt
1966ELIZA, first chatterbot (*)
1969Minsky and Papert
Lighthill Report1973
First AI Winter
$$$
1958 LISP
1980
1981
1984
Second AI Winter
Cyc
Expert systems
Expert systems disappoint, end of 5th gen
5th Generation Computing, Japan
$$$
1982 Connection Machine
(#$isa #$BarakObama #$USPresident)
(#$genls #$Tree-ThePlant #$Plant)
...
1989
1997Deep Blue
vs
Kasparov
1998 Google
1995 AltaVista
Hand-written digits recognition
BabelFish
1991 http://www
2006
2009
2010
2011
2012
2013
2015
Personal Assistant (Siri, Angie)
Fundamental DL technique cracked!
The Unreasonable Effectiveness of DataVoice breakthrough
IBM Watson wins at Jeopardy
Image breakthrough
Deep Mind plays
Atari games
Text breakthrough
TensorFlow
Google Photos
2004Project DAVE
autonomous
robot
Google Brain
So what did we learn?
Lesson: People are easy to fool!
Chess: large-scale search
Wikipedia, tricks, fast search
No real ‘understanding”, lots of data, lots of computing power
Eliza: 100% smoke and mirrors. Addictive.
Lesson: Some Things are Particularly Hard to Code
Vision
Natural Language
Speech
He said that she replied that they
could not agree. But she was wrong.
Use Rules? Use Data?
Option 1: Write a set of rules
- Use logic and heuristics to assemble hand-coded rules
- The real world is messy and changes all the time
- This approach won't scale, and it’s expensive (people)
- (We don’t have a symbol-manipulation engine in our heads)
Option 2: Learn from data
- Uses examples to find patterns automatically: the more data, the better!
- Automatically adapts to new data
- This approach scales, and it’s cheap (computing power)
Conclusion?
More data and
computing power
always beat
fancy algorithms!
Lesson: Some Problems are AI-Complete
“...I got a .45 and a shovel...”
Lesson: Some Problems are AI-Complete
“...I got a .45 and a shovel...”
“Let’s see: a .45 is a gun, and a shovel is used to dig holes. A father is usually very protective of his
daughter, and he looks intensely at the daughter's date when he says that.
Most likely interpretation: if anything happens to his daughter (accident, pregnancy), he will kill the guy
with the gun and bury him with the shovel. It’s a threat, but it can’t be serious, this is illegal, and he is
talking in front of his daughter; so it’s a funny threat, a warning. The guy will still understand that the dad
means business and expects him to take good care of his daughter.”
...
Let’s have a quick chuckle, and on to the next line…
Examples of AI-Complete Tasks
Vision
Natural Language Understanding
- Say: “Wreck a nice beach”
- Now: “Recognize speech”
Automated Translation
Virtual Assistant (Personal Assistant)
Holding a conversation
Truly summarizing text
Dealing with the real world
- Navigation
- Planning
- Adapting to new environments
Lesson: Don’t be afraid to Experiment
As we’ll learn: turn everything into numbers, mix them happily!
Don’t try to follow rigorously what is going on!
Don’t expect a mathematical proof for everything!
If you remember only one thing...
The approach that won: learn from the data!
Interlude: A.I. Movies and Memes
2001 - A Space Odyssey
Terminator
Deep Learning Class #0 - You Can Do It
Sarah - Eureka
Her
Transcendence
Ex Machina
1/ Artificial Narrow Intelligence (ANI) = Weak IA
2/ Artificial General Intelligence (AGI) = Strong IA
3/ Artificial SuperIntelligence (ASI) = 12k IQ
Art & Conception of AI
Today, computers are not smart, all the ingenuity of the
designers in AI is to make you think they are reproducing
human thought patterns.
The Future of AI: What if We Succeed?
Take a Break!
A Taste of
Deep Learning
What is Machine Learning?
= ?
question
response
prediction
1. Training a model
question prediction
2. Using the trained model
(Supervised learning)
Let’s do a (totally fake) Clinical Study
Patient id #packs per day #hours exercise per day heart problem
1 .5 2 0
2 2 0 1
3 3 6 1
4 0 5 0
5 1 0 1
6 1.5 5 0
Can we learn a pattern? Can we use it to predict outcome of new patients?
Linear Regression
Smoking
Exercising
Heart problem
Healthy heartWhat about
Sally?
Compute a linear combination of
- p = # packs per week
- h = # hours of exercise per week
Find w1, w2, w3 that best match the data.
That’s the learning.
if (score > 0) then predict heart problem.
Simple statistics.
Score > 0
Score < 0
How to find the best values for w1, w2 and w3 ?
Parameter
Error
a
b
Define error = |expected - computed|
2
Find parameters that minimize average error.
Perform Gradient Descent: quality goes up.
take a step downhill
Why “neurons”?
+
x
w1
p
x
w2
h
w3
Biological Neuron
Artificial Neuron
ChoiceScore
Linear Combination Nonlinearity
Demo: ConvNetJS
Credits and thanks to Andrej Karpathy, Stanford
https://github.com/holbertonschool/deep-learning/tree/master/ConvNetJS
You just
trained your
first model!
Feature Engineering
radius
angle
y
x
Limitations of a Linear Classifier
Food
BMI
Not so much
Healthy
starve
binge
skinny pudgy
y
x
Bad
Good
More Neurons
input1
input2
input3
Yep!
Not sure
Absolutely
78% likely
Demo: let’s make an ensemble!
More Layers: Deep Learning
input1
input2
input3
input layer hidden layers output layer
circle
iris
eye
face
89% likely to be a face
Demo: let’s go deep!
Holy Moly!
You just trained
your first deep model !!!
Why does this work?
Neural networks with at least one hidden layer can approximate any reasonably
smooth function.
Large networks have lots of solutions (minima), most of them very good.
Gradient descent is very simple and very powerful.
Everyday examples of Deep Learning
Structured data: Netflix, Spotify and YouTube recommendations; Amazon
suggestions; CC fraud detection
Text: Spam filtering; good spelling suggestions; matching ad to content;
automated translation
Images: Google Photos; search by image; FaceBook face tagging; thumbnail for
YouTube videos; OCR and handwriting recognition; surveillance videos
Voice: Android voice input; Nuance (Siri); transcription
Combo: Autonomous vehicles (soon); Virtual Assistant, Industrial robots
Deep Learning Class #0 - You Can Do It
Other Forms of Machine Learning
Unsupervised Learning
Automatically learn structure in data
Clustering
More compact representation
Semi-supervised learning
Credit: http://colah.github.io/posts/2014-10-Visualizing-MNIST/
Reinforcement Learning
Learn not from static data, but from interacting with a system
- playing a game
- flying a plane
- driving a car
- learning a task
System
do
something
get a
score
Reinforcement Learning
Learning to flip pancakes
Credit: Italian Institute of Technology
Stanford Helicopter - Andrew Ng
Driverless Car
Deep Mind plays Atari Games
DeepMind AlphaGo
Go is much harder than chess
Oct 2015: AlphaGo beats Fan Hui, a top Go player
Jan 2016: paper comes out, world goes wild
Match with Lee Sedol, #1 player, in March 2016
If you remember only one thing...
We build a model from a set of examples.
It starts as a random set of parameters.
We measure how well the predictions match the truth.
We tweak the parameters to improve this match.
A good model will generalize to new data, making useful predictions.
What we will learn
How Deep Learning works, more precisely.
What all the terms mean. It’s a big zoo.
Using existing frameworks: TensorFlow, Torch...
Downloading, using, modifying existing models.
All the tricks to tune models.
Mapping a problem to an architecture.
Take a Break!
A Taste of
Deep Learning for Images
A Brief History of
Visual Recognition
2012 - Annus Mirabilis for DL.
ImageNet contest.
Alex Krizhevsky, Ilya Sutskever,
Geoffrey Hinton, University of
Toronto
Why is Vision Hard?
Rules really don’t work for vision
I’m asking you to describe cherry blossoms.
Please use precise features and rules!
“Well, white petals arranged in a circle. Unless some of the petals have fallen.
With little white sticks and black dots arranged like this. Oh, except if seen from
the side. Or if they overlap. Or if the sun is behind. Ignore the bee…”
Next task: a human face. Any human face.
Very slow progress, even with generations of graduate students.
Demo: Google Photos
Face Recognition
Deep Learning Class #0 - You Can Do It
20102011201220132014
200320042005
2006200720082009
Glasses Hat Face paint
Tiny Overexposed Sleeping
Eating
Demo: Google Photos
Entity Recognition
horse horse horse horse
lake cup cup cup
boat snail
helicopter
mushroom frog
chairbed
bison
insect
legos
apple
churchfountain
apple mouse stuffed toy
Demo: Google Photos
Scene Recognition
bowlingscuba
green
sunset dancing
halloween birthdaysleeping
tennis lunch adventure
drinking
happiness
birthday
formal wear
How did they do that?
Deep Neural Networks
Years of Google Images, tagged by users, as training set
Face recognition is a special thing
Demo: FaceYou
Demo: ParotAI
NeuralTalk Video
Running it in Reverse
Credit: http://arxiv.org/pdf/1511.06434v2.pdf
Credit: https://github.com/Newmu/dcgan_code
Credit: https://twitter.com/vintermann/status/675599478494208000
Deep Dream by Google
An Amazing Coincidence...
J.M.Turner, 1805
Kandinsky, 1913
Pablo Picasso, 1910
Edvard Munch, 1893Vincent van Gogh, 1889
Credit: http://arxiv.org/pdf/1508.06576v1.pdf
Demo: Deep Art live
Deep Learning Class #0 - You Can Do It
Image Captioning
Source : http://googleresearch.blogspot.com/2014/11/a-picture-is-worth-thousand-coherent.html
What we will learn
Convolutional Neural Networks (ConvNets)
Adapting existing models
If you remember only one thing...
Vision is not a task that can be reduced to simple rules.
Immense progress since modern ConvNets and GPUs, ~2012.
Many real-life applications today.
Expect a lot more.
Take a Break!
A Taste of
Deep Learning for Text
Natural Language Processing (NLP)
Parsing words
Spell checking
Finding synonyms
Part of Speech (POS) Tagging
Classification
- Encoding and language detection
- Sentiment analysis
- Spam detection
- Matching ad to content
Extracting entities (people, places…)
Full-text Search
Summarization
Automated Translation
Question Answering
Virtual Assistant
- Siri, Watson, Alexa, … QA
- Her, HAL, Sarah, … Empathy & Emotion
What is so hard about
Natural Language Processing?
Understanding H2H Communication
Source : https://mishahlini1996.wordpress.com
Languages are Complex - Ambiguity
Jaguar or Jaguar ?
Languages are Complex - Context
“The Jaguar eats his prey” => predator => big cat
“The Jaguar eats the road” => image => car
Also: idioms, technical lingo, slang, humor, sarcasm, poetry, emotions...
Unsaid, but implied
“The baby looks happy!”
...
“Where are the cookies?”
Word and Sentence Similarity
Semantic Distance for Words
cat
purring
sofa
New York
dogkitten lion
Not to scale :)
serendipity
less related
2348883608
furball feline
hat
fur
turkish angora
nanocrystals
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Semantic Distance for Sentences
I like the
sushi restaurants
in Palo Alto.
A dromedary has
a single hump.
My nose is itchy!
Mind the gap!
The Japanese lunch
place near Stanford is
my favorite.
Uni is actually
sea urchin eggs.
I wish I could eat
out more often!
Demo: Sentence Similarity
Demo: Translation
Deep Learning Class #0 - You Can Do It
Deep Learning Class #0 - You Can Do It
char-nn
by Andrej Karpathy
Virtual Assistant : Car, Appliance, Robot, IoT, VR, ...
Deep Learning Class #0 - You Can Do It
Content Centric
Siri, Cortana, Alexa, ...
- Content Centric
- Question - Answering
- Light dialog
- Context Sequence(s)
- Knowledge or Actions
Far from the Human communication
HER, Sarah, HAL (or not ;p)
- People (person) Centric
- Human like dialog
- Empathy & Emotion
- Global Context
- Concept Learning
Human emotional communication
People Centric
What we will learn
How to to acquire large corpora and solve common NLP tasks.
The nltk and gensim libraries, in Python.
Vector representation for text (Embeddings).
Different examples of text classification.
The Deep Neural Networks that perform best on text: LSTM, GRU…
Generative models.
If you remember only one thing...
NLP is hard, but traditional techniques work pretty well.
Nice progress since 2012, we are getting our hands on “semantic proximity”.
Rapid progress on classification, translation.
But no true “understanding” yet.
Take a Break!
How is this
Relevant to You?
Do it with Ethic. Always!
Problem solving ?!?
Imagine how to solve one of your daily problems
through Deep Learning

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Deep Learning Class #0 - You Can Do It

  • 1. Louis Monier @louis_monier https://www.linkedin.com/in/louismonier A Taste of Deep Learning You can do it ! Gregory Renard @redo https://www.linkedin.com/in/gregoryrenard Class 0 - Q1 - 2016
  • 2. The Big Bet Credit: Google Trends
  • 3. Why we are Bullish about the Future ...100 years... ...10 years... ??? ...100 years?...
  • 4. Projections McKinsey projects $50T by 2025 - Knowledge work: big data, smart assistants, marketing, sports… - Finance, health care, genomics, law, insurance... - IoT: smart appliances, sensors, cameras, wearables - Robots, autonomous vehicles, drones, manufacturing … so VCs are pouring money into Deep Learning companies.
  • 5. A Brief History of Artificial Intelligence
  • 6. How to define Artificial Intelligence? Easy for People (intelligence) Vision, speech, understanding language Planning, common sense Having a conversation Reading emotions Translating Russian to English Playing Chess, Go Proving math theorems Easy for Machines (stamina) Multiplying huge matrices Searching large databases Sorting a trillion records Finding a path in a huge maze Hard for People and Machines Predicting the weather Predicting the winning lottery ticket
  • 7. 1770 The Mechanical Turk The “Turing Test”1950 1956Dartmouth Conference Checkers-playing program “Magic” 1957 Perceptrons, Frank Rosenblatt 1966ELIZA, first chatterbot (*) 1969Minsky and Papert Lighthill Report1973 First AI Winter $$$ 1958 LISP
  • 8. 1980 1981 1984 Second AI Winter Cyc Expert systems Expert systems disappoint, end of 5th gen 5th Generation Computing, Japan $$$ 1982 Connection Machine (#$isa #$BarakObama #$USPresident) (#$genls #$Tree-ThePlant #$Plant) ... 1989 1997Deep Blue vs Kasparov 1998 Google 1995 AltaVista Hand-written digits recognition BabelFish 1991 http://www
  • 9. 2006 2009 2010 2011 2012 2013 2015 Personal Assistant (Siri, Angie) Fundamental DL technique cracked! The Unreasonable Effectiveness of DataVoice breakthrough IBM Watson wins at Jeopardy Image breakthrough Deep Mind plays Atari games Text breakthrough TensorFlow Google Photos 2004Project DAVE autonomous robot Google Brain
  • 10. So what did we learn?
  • 11. Lesson: People are easy to fool! Chess: large-scale search Wikipedia, tricks, fast search No real ‘understanding”, lots of data, lots of computing power Eliza: 100% smoke and mirrors. Addictive.
  • 12. Lesson: Some Things are Particularly Hard to Code Vision Natural Language Speech He said that she replied that they could not agree. But she was wrong.
  • 13. Use Rules? Use Data? Option 1: Write a set of rules - Use logic and heuristics to assemble hand-coded rules - The real world is messy and changes all the time - This approach won't scale, and it’s expensive (people) - (We don’t have a symbol-manipulation engine in our heads) Option 2: Learn from data - Uses examples to find patterns automatically: the more data, the better! - Automatically adapts to new data - This approach scales, and it’s cheap (computing power)
  • 14. Conclusion? More data and computing power always beat fancy algorithms!
  • 15. Lesson: Some Problems are AI-Complete “...I got a .45 and a shovel...”
  • 16. Lesson: Some Problems are AI-Complete “...I got a .45 and a shovel...” “Let’s see: a .45 is a gun, and a shovel is used to dig holes. A father is usually very protective of his daughter, and he looks intensely at the daughter's date when he says that. Most likely interpretation: if anything happens to his daughter (accident, pregnancy), he will kill the guy with the gun and bury him with the shovel. It’s a threat, but it can’t be serious, this is illegal, and he is talking in front of his daughter; so it’s a funny threat, a warning. The guy will still understand that the dad means business and expects him to take good care of his daughter.” ... Let’s have a quick chuckle, and on to the next line…
  • 17. Examples of AI-Complete Tasks Vision Natural Language Understanding - Say: “Wreck a nice beach” - Now: “Recognize speech” Automated Translation Virtual Assistant (Personal Assistant) Holding a conversation Truly summarizing text Dealing with the real world - Navigation - Planning - Adapting to new environments
  • 18. Lesson: Don’t be afraid to Experiment As we’ll learn: turn everything into numbers, mix them happily! Don’t try to follow rigorously what is going on! Don’t expect a mathematical proof for everything!
  • 19. If you remember only one thing... The approach that won: learn from the data!
  • 21. 2001 - A Space Odyssey
  • 25. Her
  • 28. 1/ Artificial Narrow Intelligence (ANI) = Weak IA 2/ Artificial General Intelligence (AGI) = Strong IA 3/ Artificial SuperIntelligence (ASI) = 12k IQ Art & Conception of AI Today, computers are not smart, all the ingenuity of the designers in AI is to make you think they are reproducing human thought patterns. The Future of AI: What if We Succeed?
  • 30. A Taste of Deep Learning
  • 31. What is Machine Learning? = ? question response prediction 1. Training a model question prediction 2. Using the trained model (Supervised learning)
  • 32. Let’s do a (totally fake) Clinical Study Patient id #packs per day #hours exercise per day heart problem 1 .5 2 0 2 2 0 1 3 3 6 1 4 0 5 0 5 1 0 1 6 1.5 5 0 Can we learn a pattern? Can we use it to predict outcome of new patients?
  • 33. Linear Regression Smoking Exercising Heart problem Healthy heartWhat about Sally? Compute a linear combination of - p = # packs per week - h = # hours of exercise per week Find w1, w2, w3 that best match the data. That’s the learning. if (score > 0) then predict heart problem. Simple statistics. Score > 0 Score < 0
  • 34. How to find the best values for w1, w2 and w3 ? Parameter Error a b Define error = |expected - computed| 2 Find parameters that minimize average error. Perform Gradient Descent: quality goes up. take a step downhill
  • 35. Why “neurons”? + x w1 p x w2 h w3 Biological Neuron Artificial Neuron ChoiceScore Linear Combination Nonlinearity
  • 36. Demo: ConvNetJS Credits and thanks to Andrej Karpathy, Stanford https://github.com/holbertonschool/deep-learning/tree/master/ConvNetJS
  • 39. Limitations of a Linear Classifier Food BMI Not so much Healthy starve binge skinny pudgy y x Bad Good
  • 41. Demo: let’s make an ensemble!
  • 42. More Layers: Deep Learning input1 input2 input3 input layer hidden layers output layer circle iris eye face 89% likely to be a face
  • 44. Holy Moly! You just trained your first deep model !!!
  • 45. Why does this work? Neural networks with at least one hidden layer can approximate any reasonably smooth function. Large networks have lots of solutions (minima), most of them very good. Gradient descent is very simple and very powerful.
  • 46. Everyday examples of Deep Learning Structured data: Netflix, Spotify and YouTube recommendations; Amazon suggestions; CC fraud detection Text: Spam filtering; good spelling suggestions; matching ad to content; automated translation Images: Google Photos; search by image; FaceBook face tagging; thumbnail for YouTube videos; OCR and handwriting recognition; surveillance videos Voice: Android voice input; Nuance (Siri); transcription Combo: Autonomous vehicles (soon); Virtual Assistant, Industrial robots
  • 48. Other Forms of Machine Learning
  • 49. Unsupervised Learning Automatically learn structure in data Clustering More compact representation Semi-supervised learning Credit: http://colah.github.io/posts/2014-10-Visualizing-MNIST/
  • 50. Reinforcement Learning Learn not from static data, but from interacting with a system - playing a game - flying a plane - driving a car - learning a task System do something get a score
  • 52. Learning to flip pancakes Credit: Italian Institute of Technology
  • 55. Deep Mind plays Atari Games
  • 56. DeepMind AlphaGo Go is much harder than chess Oct 2015: AlphaGo beats Fan Hui, a top Go player Jan 2016: paper comes out, world goes wild Match with Lee Sedol, #1 player, in March 2016
  • 57. If you remember only one thing... We build a model from a set of examples. It starts as a random set of parameters. We measure how well the predictions match the truth. We tweak the parameters to improve this match. A good model will generalize to new data, making useful predictions.
  • 58. What we will learn How Deep Learning works, more precisely. What all the terms mean. It’s a big zoo. Using existing frameworks: TensorFlow, Torch... Downloading, using, modifying existing models. All the tricks to tune models. Mapping a problem to an architecture.
  • 60. A Taste of Deep Learning for Images
  • 61. A Brief History of Visual Recognition 2012 - Annus Mirabilis for DL. ImageNet contest. Alex Krizhevsky, Ilya Sutskever, Geoffrey Hinton, University of Toronto
  • 62. Why is Vision Hard?
  • 63. Rules really don’t work for vision I’m asking you to describe cherry blossoms. Please use precise features and rules! “Well, white petals arranged in a circle. Unless some of the petals have fallen. With little white sticks and black dots arranged like this. Oh, except if seen from the side. Or if they overlap. Or if the sun is behind. Ignore the bee…” Next task: a human face. Any human face. Very slow progress, even with generations of graduate students.
  • 68. Glasses Hat Face paint Tiny Overexposed Sleeping Eating
  • 70. horse horse horse horse lake cup cup cup
  • 73. bowlingscuba green sunset dancing halloween birthdaysleeping tennis lunch adventure drinking happiness birthday formal wear
  • 74. How did they do that? Deep Neural Networks Years of Google Images, tagged by users, as training set Face recognition is a special thing
  • 78. Running it in Reverse
  • 82. Deep Dream by Google
  • 84. J.M.Turner, 1805 Kandinsky, 1913 Pablo Picasso, 1910 Edvard Munch, 1893Vincent van Gogh, 1889 Credit: http://arxiv.org/pdf/1508.06576v1.pdf
  • 87. Image Captioning Source : http://googleresearch.blogspot.com/2014/11/a-picture-is-worth-thousand-coherent.html
  • 88. What we will learn Convolutional Neural Networks (ConvNets) Adapting existing models
  • 89. If you remember only one thing... Vision is not a task that can be reduced to simple rules. Immense progress since modern ConvNets and GPUs, ~2012. Many real-life applications today. Expect a lot more.
  • 91. A Taste of Deep Learning for Text
  • 92. Natural Language Processing (NLP) Parsing words Spell checking Finding synonyms Part of Speech (POS) Tagging Classification - Encoding and language detection - Sentiment analysis - Spam detection - Matching ad to content Extracting entities (people, places…) Full-text Search Summarization Automated Translation Question Answering Virtual Assistant - Siri, Watson, Alexa, … QA - Her, HAL, Sarah, … Empathy & Emotion
  • 93. What is so hard about Natural Language Processing?
  • 94. Understanding H2H Communication Source : https://mishahlini1996.wordpress.com
  • 95. Languages are Complex - Ambiguity Jaguar or Jaguar ?
  • 96. Languages are Complex - Context “The Jaguar eats his prey” => predator => big cat “The Jaguar eats the road” => image => car Also: idioms, technical lingo, slang, humor, sarcasm, poetry, emotions...
  • 97. Unsaid, but implied “The baby looks happy!” ... “Where are the cookies?”
  • 98. Word and Sentence Similarity
  • 99. Semantic Distance for Words cat purring sofa New York dogkitten lion Not to scale :) serendipity less related 2348883608 furball feline hat fur turkish angora nanocrystals
  • 100. Terms similar to Champagne french champagne, cognac, champagne's, champagnes, veuve clicquot, cremant, louis roederer, rosé, taittinger, fine champagne, champagne wine, sparkling wines, dom pérignon, dom perignon, pol roger, vintage champagne, bubblies, pommery, rose wine, pink wine, blancs, french wine, cliquot, beaujolais nouveau, sancerre, sparkling, burgundy, chateau, chablis, cognacs, pink champagne, domaine, moët, methode champenoise, burgundy wines, apéritif, armagnac, chandon, champenoise, beaujolais, heidsieck, marnier, wine, bourgogne, aperitif, chateau margaux, demi-sec, moelleux, champagne cocktail, crémant, half-bottle, cuvée, brut, ruinart, champagne flute, st emilion, white wine, loire valley, wine cocktail, veuve, drinking champagne, french wines, blanc, chardonnay wine, champagne glass, cuvées, mauzac, roederer estate, laurent- perrier, puligny, negociant, prosecco, rose wines, gloria ferrer, red wine, musigny, coteaux, corton-charlemagne, fine wine, dessert wine, bordeaux, champagne glasses, cheval blanc, champagne flutes, cuvees, champange, four wines, montlouis, rémy martin, primeur, fine wines, lirac, d'yquem, burgundy wine, red bordeaux, brandy, cuvee, white burgundy, chardonnay, chambolle-musigny, cheverny, great vintages, yquem, special wines, wonderful wines, burgundies, half bottles, grand marnier, grand cru, primeurs, sauterne, minervois, pouilly-fuissé, sauternes, chambertin, white bordeaux, vougeot, epernay, vin gris, chalonnaise, quaffer, loire, sweet white wine, d'aunis, côtes, gevrey-chambertin, limoux, english wine, chateaux, château haut-brion, blanche, pinot meunier, six glasses, mâconnais, épernay, bourbon, sparkler, volnay, white wines, chassagne-montrachet, burgundys, vin jaune, claret, beaune, grande champagne, white grapes, bordeaux wine, dessert wines, crème de cassis, pinot noir grapes, chardonnay grapes, armand de brignac, select wines, calvados, country wine, muscadet, leflaive, reisling, cointreau, own wine, caveau, clos de vougeot, inexpensive wines, vosne-romanée..., expensive wines, red burgundy, barsac, delicious wine, wine flight, puligny-montrachet, rousanne, châteauneuf-du-pape, liqueur, schramsberg, touraine, montrachet, arbois, lanson, vintage wine, chateauneuf, blanquette, non-vintage, orange wine, three wines, wine.the, banyuls, merlot wine, vendange, red table wine, sweet wines, santenay, languedoc, moscato d'asti …
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  • 104. Semantic Distance for Sentences I like the sushi restaurants in Palo Alto. A dromedary has a single hump. My nose is itchy! Mind the gap! The Japanese lunch place near Stanford is my favorite. Uni is actually sea urchin eggs. I wish I could eat out more often!
  • 110. Virtual Assistant : Car, Appliance, Robot, IoT, VR, ...
  • 112. Content Centric Siri, Cortana, Alexa, ... - Content Centric - Question - Answering - Light dialog - Context Sequence(s) - Knowledge or Actions Far from the Human communication HER, Sarah, HAL (or not ;p) - People (person) Centric - Human like dialog - Empathy & Emotion - Global Context - Concept Learning Human emotional communication People Centric
  • 113. What we will learn How to to acquire large corpora and solve common NLP tasks. The nltk and gensim libraries, in Python. Vector representation for text (Embeddings). Different examples of text classification. The Deep Neural Networks that perform best on text: LSTM, GRU… Generative models.
  • 114. If you remember only one thing... NLP is hard, but traditional techniques work pretty well. Nice progress since 2012, we are getting our hands on “semantic proximity”. Rapid progress on classification, translation. But no true “understanding” yet.
  • 117. Do it with Ethic. Always!
  • 118. Problem solving ?!? Imagine how to solve one of your daily problems through Deep Learning