7. RADIX.AI I
Old way:
Write software with explicit rules to
follow:
if email contains V!agrĂĽ
then mark is-spam;
if email contains âŚ
if email contains âŚ
New way:
Write software to learn from examples:
try to classify some emails;
change self to reduce errors
repeat;
7
Machine Learning is a new way of programming
10. RADIX.AI I
Step 2: train the modelReal Estate Prices, Ixelles, Belgium, 2018
⏠400.000
⏠300.000
⏠200.000
output
70m2
100m2
130m2
input
model
11. RADIX.AI I
Step 3: predictReal Estate Prices, Ixelles, Belgium, 2018
⏠400.000
output
120 m2
input
model
12. RADIX.AI I
input â model â output
Appartement 2 Bedrooms
1050 Ixelles
140 m2
1980
Gas
...has a large terrace with a
nice view on the parc ...
13. RADIX.AI I
input â model â output
income
Decision Trees Neural Networks
savings
income
savings
income
savings
Logistic Regression
14. RADIX.AI I
House price
Days until sale
Number of
candidates
input â model â output
Chance of being sold
within a year
Chance of defaulting
on the loan
Chance of flooding
Loan yes/no
Picture is interior or
exterior
Best channel to
advertise a house
The output of a model can be a number, a probability, or a category.
123,45 83% A / B / C
15. RADIX.AI I
Look at your goal. Look at your data. Map.
How to recognize machine learning opportunities.
Goal:
- Telecom company
- Wants to Customer Lifetime Value
Data:
- User behaviour
- User demographics
- User billing
- User network
- User social media
Use Cases:
- Churn prediction
- Personalised offers
Output:
- 10% most likely churners
- Reasons for churn
21. RADIX.AI I
Why DL? Feed any data type!
We are looking for an
educationally focused
Governess for a family
who live between Dubai
and London. The summer
months are spent in
London and Europe and
there is travel
throughout the year.
This is a sole charge
position as both
parents work. You will
be responsible for two
children aged 18 months
& 3 years old.
Never gonna give you up
Male dancing
Traveling nanny
22. RADIX.AI I
Why DL? Scale improves performance!
22
Large Neural Network
Amount of data
ModelPerformance
Traditional ML models
E.g., SVM, GP, RF, LR
ML
DL
23. RADIX.AI I
Why DL? Useful embeddings!
23
Useful word
embedding
h1 ⯠âŻh2
hNBabysitter
word
30. RADIX.AI I
New technologies = new possibilities
30
1050 Ixelles
Babysitter
EN
License B
1000 Brussels
Nanny
⌠speaks English âŚ
⌠drive the kids
to school âŚ
34. RADIX.AI I
Job Recommender Architecture
34
Job
embedding
Job Seeker
embedding
.9
Similarity
Complex data Embedding
300 numbers
Score
1 number
h1 ⯠âŻh2
hN
h1 ⯠âŻh2
hN
RADIX.AI I 34
35. RADIX.AI I
Embeddings are steered by click data
35
Job
embedding
Job Seeker
embedding
.9
Similarity
Complex data Embedding
300 numbers
Score
1 number
h1 ⯠âŻh2
hN
h1 ⯠âŻh2
hN
RADIX.AI I 35
36. RADIX.AI I
In production soon at vdab.be!
36
1050 Ixelles
Babysitter
EN, FR
License B
1000 Brussels
Nanny
⌠speaks English
and French âŚ
⌠drive the kids
to school âŚ
37. RADIX.AI I
hello@radix.ai
37
Deep Learning
At our core, we are a
team of Machine
Learning engineers.
We make machines
learn.
3
Natural
Language
Processing
Understanding natural
language is not easy.
Itâs our favorite
challenge.
Job Matching
Matching jobs and job
seekers.
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2