Introduction to Machine Learning for newcomers. It will show you some basic concepts like what is supervised learning, unsupervised learning, classification, regression, under/overfitting, clustering, anomaly detection, and how to have some measures. It will illustrates examples through scikit-learn and tensorflow code

1. B2B Travel technology
Machine Learning Introduction
October 20th 2016

2. Introduction
Johnny RAHAJARISON
@brainstorm_me
johnny.rahajarison@mylittleadventure.com
MyLittleAdventure
@mylitadventure
2

3. Agenda
What’s Machine Learning ?
Usage examples
Complexity
Algorithm families
Let’s go!
Troubleshoot
Tech insights
Next steps
Conclusion
3!

4. Machine learning
Introduction
4

5. What’s Machine Learning ?
Software that do something without being explicitly
programmed to, just by learning through examples
Same software can be used for various tasks
It learns from experiences with respect to some task and performance,
and improves through experience
5!

6. Usage examples (1/2)
6!
Some typical usage examples

7. Use cases : MyLittleAdventure (2/2)
7
Language detection
Clustering
Anomaly detection
Recommendation
Chose of parameters
MyLittleAdventure usage
!

8. Complexity
8!
"""Tests for convolution related functionality in tensorflow.ops.nn."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
from six.moves import xrange # pylint: disable=redefined-builtin
import tensorflow as tf
class Conv2DTransposeTest(tf.test.TestCase):
def testConv2DTransposeSingleStride(self):
with self.test_session():
strides = [1, 1, 1, 1]
# Input, output: [batch, height, width, depth]
x_shape = [2, 6, 4, 3]
y_shape = [2, 6, 4, 2]
# Filter: [kernel_height, kernel_width, output_depth, input_depth]
f_shape = [3, 3, 2, 3]
x = tf.constant(1.0, shape=x_shape, name="x", dtype=tf.float32)
f = tf.constant(1.0, shape=f_shape, name="filter", dtype=tf.float32)
output = tf.nn.conv2d_transpose(x, f, y_shape, strides=strides,
padding="SAME")
value = output.eval()
# We count the number of cells being added at the locations in the output.
# At the center, #cells=kernel_height * kernel_width
# At the corners, #cells=ceil(kernel_height/2) * ceil(kernel_width/2)
# At the borders, #cells=ceil(kernel_height/2)*kernel_width or
# kernel_height * ceil(kernel_width/2)
for n in xrange(x_shape[0]):
for k in xrange(f_shape[2]):
for w in xrange(y_shape[2]):
for h in xrange(y_shape[1]):
target = 4 * 3.0
h_in = h > 0 and h < y_shape[1] - 1
w_in = w > 0 and w < y_shape[2] - 1
if h_in and w_in:
target += 5 * 3.0
"""GradientDescent for TensorFlow."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from tensorflow.python.framework import ops
from tensorflow.python.ops import math_ops
from tensorflow.python.training import optimizer
from tensorflow.python.training import training_ops
class GradientDescentOptimizer(optimizer.Optimizer):
"""Optimizer that implements the gradient descent algorithm.
@@__init__
"""
def __init__(self, learning_rate, use_locking=False, name="GradientDescent"):
"""Construct a new gradient descent optimizer.
Args:
learning_rate: A Tensor or a floating point value. The learning
rate to use.
use_locking: If True use locks for update operations.
name: Optional name prefix for the operations created when applying
gradients. Defaults to "GradientDescent".
"""
super(GradientDescentOptimizer, self).__init__(use_locking, name)
self._learning_rate = learning_rate
def _apply_dense(self, grad, var):
return training_ops.apply_gradient_descent(
var,
math_ops.cast(self._learning_rate_tensor, var.dtype.base_dtype),
grad,
use_locking=self._use_locking).op
def _apply_sparse(self, grad, var):
delta = ops.IndexedSlices(
grad.values *
"""Tests for tensorflow.ops.linalg_grad."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
import tensorflow as tf
class ShapeTest(tf.test.TestCase):
def testBatchGradientUnknownSize(self):
with self.test_session():
batch_size = tf.constant(3)
matrix_size = tf.constant(4)
batch_identity = tf.tile(
tf.expand_dims(
tf.diag(tf.ones([matrix_size])), 0), [batch_size, 1, 1])
determinants = tf.matrix_determinant(batch_identity)
reduced = tf.reduce_sum(determinants)
sum_grad = tf.gradients(reduced, batch_identity)[0]
self.assertAllClose(batch_identity.eval(), sum_grad.eval())
class MatrixUnaryFunctorGradientTest(tf.test.TestCase):
pass # Filled in below
def _GetMatrixUnaryFunctorGradientTest(functor_, dtype_, shape_, **kwargs_):
def Test(self):
with self.test_session():
np.random.seed(1)
m = np.random.uniform(low=-1.0,
high=1.0,
size=np.prod(shape_)).reshape(shape_).astype(dtype_)
a = tf.constant(m)
b = functor_(a, **kwargs_)
# Optimal stepsize for central difference is O(epsilon^{1/3}).
epsilon = np.finfo(dtype_).eps
delta = 0.1 * epsilon**(1.0 / 3.0)
# tolerance obtained by looking at actual differences using
# np.linalg.norm(theoretical-numerical, np.inf) on -mavx build
Complex algorithm before
train_step = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy)
And Machine learning now…

9. Machine learning
Algorithm families
9

10. Supervised algorithms
10
Supervised algorithms
ClassificationRegression

11. Unsupervised algorithms
11
Unsupervised algorithms
ClusteringAnomaly detection

12. Machine learning
Let’s go!
12

13. Recipe
!13
Collect
Training data
Files, database,
cache, data flow
Selection of model,
and (hyper) parameters
Train algorithm
Use or store your
trained estimator
Make predictions
Measure accuracy
precision
Measure

14. Collect training data
Get qualitative data
Get some samples
Don’t get data for months and then try
Go fast and try things.
14
Weight (g) Width (cm) Height (cm) Label
192 8.4 7.3 Granny smith apple
86 6.2 4.7 Mandarin
178 7.1 7.8 Braeburn apple
162 7.4 7.2 Cripps pink apple
118 6.1 8.1 Unidentified lemons
144 6.8 7.4 Turkey orange
362 9.6 9.2 Spanish jumbo orange
… … … …
What about the data?
Fruit identification example

15. Prepare your data
15
Numerize your features and labels
Put them in same scale (normalization) ?
Weight (g) Width (cm) Height (cm) Label
192 8.4 7.3 1
86 6.2 4.7 2
178 7.1 7.8 3
162 7.4 7.2 5
118 6.1 8.1 10
144 6.8 7.4 8
362 9.6 9.2 9
… … … …
We need to have some tests
Training set
Learning phase (60% - 80 %)
Test set
Analytics phase (20% - 40%)

16. 16
Prepare your data (code)

17. Train algorithm
17
Choose a classifier
Fit the decision tree
Weight (g) Width (cm) Height (cm) Label
192 8.4 7.3 1
86 6.2 4.7 2
178 7.1 7.8 3
162 7.4 7.2 5
118 6.1 8.1 10
144 6.8 7.4 8
362 9.6 9.2 9
… … … …
We need to choose an estimator

18. Make predictions
18
What looks our predictions?
Weight
(g)
Width
(cm)
Height
(cm)
Label
192 8.4 7.3 1
86 6.2 4.7 2
178 7.1 7.8 3
Test set
Weight
(g)
Width
(cm)
Height
(cm)
Label
192 8.4 7.3 1
86 6.2 4.7 2
178 7.1 7.8 1
Predictions
!

19. Measure (1/2)
Evaluate on the dataset that as never ever been learned by your model
19!
Accuracy
Correct predictions / total predictions
Gives a simple confidence score of our performance level

20. Measure (2/2)
Try to visualize and analyze your data, and know what you want
20!
Actual true Actual false
Predicted
true
True
positive
False
positive
Predicted
false
False
negative
True
negative
Confusion Matrix
Skewed classes
Precision = True positives / #predicted positives
Recall = True positives / #actual positives
F1 score (trade-off) = (precision * recall) /
(precision + recall)

21. 21
Measure and prediction (code)

22. Machine learning
Troubleshoot
22

23. Troubleshoot (1/4)
23!
Under/Overfitting situation

24. Troubleshoot (2/4)
Underfitting
Add / create more features
Use more sophisticated model
Use fewer samples
Decrease regularization
24!
Overfitting
Use fewer features
Use more simple model
Use more samples
Increase regularization
What are the different options ?

25. Troubleshoot (3/4)
25!
Underfitting Overfitting
Using the learning curves…

26. Troubleshoot : Model choice (4/4)
26!

27. Machine learning
Tech insights
27

28. Platforms : easy, peasy
You don’t even have to code to build something
(*wink wink* business developers)
Built-in models
Data munging
Model management by UI
PaaS
28!
Very high-level solutions

29. Languages
For understanding &
prototyping implementation
Most Valuable Languages
Comfortable for prototyping,
yet powerful for
industrialisation
For bigger companies &
projects, and fine-tuned
softwares
29!
Matlab Octave Go Python Java C++
What language for what purpose ?

30. Libraries
Built-in models
Data munging
Fine-tuning
Full integration to your product
30!
You will have great power using a library
Golearn

31. Machine learning
Next steps…
31

32. Next steps
Split your data in 3 : Training / Cross validation / Test set
Know the top algorithms
Search advanced techniques and optimizers (online learning, stacking)
Deep and reinforcement learning
Partial and semi-supervised learning
Transfer learning
How to store and analyse big data ? How do we scale ?
!32
Try it ! Find your best tools and have some fun

33. Conclusion
Try it and let’s get in touch!
Machine learning is not just a buzz word
Difficulties are not always what we think!
Machine learning is rather experiences and tests than just algorithms
There is no perfect unique solution
There is plenty of easy to use solutions for beginners
33!

34. Machine learning
One more thing!
34

35. Tensorflow
35

36. Tensorflow learn
36

37. Thank you
Machine Learning Introduction
October 20th 2016
Questions ?