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Machine Learning
Boris Nadion
boris@astrails.com
@borisnadion
@borisnadion
boris@astrails.com
astrails
http://astrails.com
awesome web and mobile apps
since 2005
terms
AI (artificial intelligence)
- the theory and development of computer systems
able to perform tasks that normally require human
intelligence, such as visual perception, speech
recognition, decision-making, and translation
between languages
ML (machine learning)
- is a type of artificial intelligence (AI) that provides
computers with the ability to learn without being
explicitly programmed. Machine learning focuses
on the development of computer programs that
can teach themselves to grow and change when
exposed to new data.
without being explicitly programmed
FF NN cost function
FF NN Cost Function
I’m kidding
cost function with regularization
2 types of ML
supervised learning
unsupervised learning
supervised
the training data is labeled,
eg. we know the correct answer
unsupervised
the training data is not labeled,
eg. we would figure out hidden correlations by ourselves
linear regression
supervised learning
(x(1)
, y(1)
), (x(2)
, y(2)
)…(x(m)
, y(m)
)
m training examples of (x(i)
, y(i)
)
x(i)
- feature
y(i)
- label
x(i)
y(i)
training set
learning algorithm
hθ(x)x(new data)
y(prediction)
training set
learning algorithm
hθ(x)x(new data)
y(prediction)
hθ(x) = hypothesis
(x, y)
y = hθ(x) = θ0 + θ1x
find θ0 and θ1
hθ(x) = θ0 + θ1x1 + θ2x2 + …+ θnxn
many features, n - number of features
size, sq.m
x1
# rooms
x2
age
x3
price
y
80 3 22 2.9M
90 4 24 3.1M
75 3 28 2.5M
110 5 20 3.3M
1 USD = 3.85 NIS
hθ(x) = θ0 + θ1x1
summate the prediction error on training set
Linear Regression Cost Function
minimize J(θ)
funding a minimum of cost function = “learning”
gradient descent
batch, stochastic, etc, or advanced optimization algorithms
to find a global (sometimes local) minimum of cost function J
𝞪 - learning rate, a parameter of gradient descent
(x(1)
, y(1)
), (x(2)
, y(2)
)…(x(m)
, y(m)
)
gradient descent
θ0, θ1, θ2, …, θn
magic
inside
hθ(x) = θ0 + θ1x1 + θ2x2 + …+ θnxn
we’re ready to predict
features scaling
0 ≤ x ≤ 1
size, sq.m
size, sq.m / 110
x1
80 0.72
90 0.81
75 0.68
110 1
mean normalization
average value of the feature is ~0
-0.5 ≤ x ≤ 0.5
size, sq.m
(size, sq.m / 110) - 0.8025
x1
80 -0.0825
90 0.075
75 -0.1226
110 0.1975
matrix manipulations
X = n x 1 vector, ϴ = n x 1 vector
hθ(x) = θ0 + θ1x1 + θ2x2 + …+ θnxn
hθ(x) = ϴT
X
GPU
logistic regression
supervised learning
classifier
y = 1, true
y = 0, false
hθ(x) = g(ϴT
X)
hθ(X) - estimated probability that y = 1 on input X
g(z) - logistic non-linear function
logistic function g(z)
there is a few: sigmoid, tahn, ReLUs, etc
image source: Wikipedia
(x(1)
, y(1)
), (x(2)
, y(2)
)…(x(m)
, y(m)
)
minimize the cost
function
vector θ
y = {0, 1}
training set
learning algorithm
hθ(x)x(new data)
y(prediction)
hθ(x) = g(ϴT
X) y ≥ 0.5 - true
y < 0.5 - false
one-vs-all
supervised learning
y = 1, true
y = 0, false
y = 0, false
don’t implement it at home
use libsvm, liblinear, and others
neural networks
supervised learning
neuron
a0
a1
a2
computation hθ(a)
feed forward neural network
input layer
hidden layer
output layer
estimates
size, sq.m
# rooms
age
e0
e1
e2
e3
estimates
final
estimate
multiclass classifiers
logistic unit
x0
x1
x2
θ1
θ2
θ3 hθ = g(x0θ0 + x1θ1 + x2θ2)
θ - weights
g - activation function
logistic function g(z)
there is a few: sigmoid, tahn, ReLUs, etc
image source: Wikipedia
output: probabilities
0.4765 that y = 2
0.7123 that y = 1
net with no hidden layers
no hidden layers = one-vs-all logistic regression
cost function
sometimes called loss function of NN,
a representation of an error between
a real and a predicted value
training set
learning algorithm
θx(new data)
y(prediction)
backprop
backward propagation of errors
gradient descent + backprop
“deep learning” - is training a neural net
“deep” - because we have many layers
convolutional neural nets
widely used for image processing and object recognition
recurrent neural nets
widely used for natural language processing
CPU/GPU expensive
image source: https://xkcd.com/303/
image source: http://www.falsepositives.com/index.php/2008/01/31/the-real-reason-for-no-increased-productivity-behind-scripting-languages-reveled/
2008
2016
destination suggestion
tangledpath/ruby-fann
Ruby library for interfacing with FANN
(Fast Artificial Neural Network)
require './neural_network'
LOCATIONS = [:home, :work, :tennis, :parents]
LOCATIONS_INDEXED = LOCATIONS.map.with_index { |x, i| [x, i] }.to_h
XX = [
# week 1
# 1st day of week, 8am
[:work, 1, 8], [:tennis, 1, 17], [:home, 1, 20],
[:work, 2, 8], [:home, 2, 18],
[:work, 3, 8], [:tennis, 3, 17], [:home, 3, 20],
[:work, 4, 8], [:home, 4, 18],
[:work, 5, 8], [:home, 5, 18],
[:parents, 7, 13], [:home, 7, 18],
# week 2
[:work, 1, 8], [:home, 1, 18],
[:work, 2, 8], [:home, 2, 18],
[:work, 3, 8], [:tennis, 3, 17], [:home, 3, 20],
[:work, 4, 8], [:home, 4, 18],
[:work, 5, 8], [:home, 5, 18],
XX.each do |destination, day, time|
yy << LOCATIONS_INDEXED[destination]
xx << [day.to_f/7, time.to_f/24]
end
features scaling
2 ➞ 25 ➞ 4
one hidden layer with 25 units
100% accuracy
on training set
[
[1, 16.5], [1, 17], [1, 17.5], [1, 17.8],
[2, 17], [2, 18.1],
[4, 18],
[6, 23],
[7, 13],
].each do |day, time|
res = nn.predict_with_probabilities([
[day.to_f/7, time.to_f/24]
]).first.
select {|v| v[0] > 0} # filter zero probabilities
puts "#{day} #{time} t #{res.map {|v| [LOCATIONS[v[1]], v[0]]}.inspect}"
end
1 16.5 [[:tennis , 0.97]]
1 17 [[:tennis , 0.86], [:home , 0.06]]
1 17.5 [[:home , 0.52], [:tennis, 0.49]]
1 17.8 [[:home , 0.82], [:tennis, 0.22]]
2 17 [[:tennis , 0.85], [:home , 0.06]]
2 18.1 [[:home , 0.95], [:tennis, 0.07]]
4 18 [[:home , 0.96], [:tennis, 0.08]]
6 23 [[:home , 1.00]]
[:work, 1, 8], [:tennis, 1, 17], [:home, 1, 20],
[:work, 2, 8], [:home, 2, 18],
[:work, 3, 8], [:tennis, 3, 17], [:home, 3, 20],
[:work, 4, 8], [:home, 4, 18],
[:work, 5, 8], [:home, 5, 18],
[:parents, 7, 13], [:home, 7, 18],
# week 2
[:work, 1, 8], [:home, 1, 18],
[:work, 2, 8], [:home, 2, 18],
[:work, 3, 8], [:tennis, 3, 17], [:home, 3, 20],
[:work, 4, 8], [:home, 4, 18],
[:work, 5, 8], [:home, 5, 18],
borisnadion/suggested-destination-demo
ruby code of the demo
tensorflow
but you will need to learn Python
clustering
unsupervised learning
{X(i)
}
no labels
anomaly detection
unsupervised learning
collaborative filtering
unsupervised learning
Jane Arthur John
Star Wars VII 5 5 1
Dr. Strange 5 5 ?
Arrival 5 ? 1
automatic features
and their weights detection
based on the user votes
similarity between users
and between items
what to google
http://astrails.com
thanks!
Boris Nadion
http://astrails.com

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