Yufeng Guo | Coding the 7 steps of machine learning | Codemotion Madrid 2018

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Machine learning has gained a lot of attention as the next big thing. But what is it, really, and how can we use it? In this talk, you'll learn the meaning behind buzzwords like hyperparameter tuning, and see the code behind each step of machine learning. This talk will help demystify the "magic" behind machine learning. You'll come away with a foundation that you can build on, and an understanding of the tools to build with!

Technology

Coding
the 7 Steps
of Machine Learning
Yufeng Guo
@YufengG

Machine Learning
is
programming
with data.

7 Steps of Machine Learning
1. Gathering Data
2. Preparing that Data
3. Choosing a Model
4. Training
5. Evaluation
6. Hyperparameter Tuning
7. Prediction

tech
sport
business
politics
entertainment

DATA:
UK mobile owners continue to
break records with their text
messaging, with latest figures
showing that 26 billion texts
were sent in total in 2004.
?
PredictionModel
tech
sport
business
politics
entertainment

1. Gathering Data
Often overlooked, always undervalued

SELECT
category,
TRIM(LOWER(REGEXP_REPLACE(
CONCAT(title, ' ', body), r'["n'?,]|<p>|</p>'," "
))) as text
FROM `bigquery-public-data.bbc_news.fulltext`
ORDER BY RAND()
Gathering data from BigQuery

2. Preparing that Data
Can take a bit longer than expected

coach
oscar
wednesday
oil
gadgets
animals
Building a bag of words model: a simple example
Vocabulary Possible labels
tech
sport
business
politics
entertainment
animals
measures
europe
theatre
lawyer
cats

coach
oscar
wednesday
oil
gadgets
animals
Building a bag of words model: a simple example
Vocabulary
animals
electronics
europe
theatre
lawyer
cats
Inputs
gadgets on show at the 2005
consumer electronics show

Building a bag of words model: a simple example
Labels
tech
sport
business
politics
entertainment
[ 0 1 0 0 0 ]

Building a bag of words model: a simple example
Labels
tech
sport
business
politics
entertainment
[ 0.01 0.92 0.03 0.02 0.01 ]

Building a bag of words model: a simple example
Labels
tech
sport
business
politics
entertainment
[ 0.03 0.02 0.04 0.88 0.03 ]

To the code!
bit.ly/kaggle-bbc-code
kaggle.com/yufengdev/bbc-text-categoriz
ation

3. Choosing a Model
Everyone's favorite conversation topic

Android iOS ...GPUCPU
TensorFlow Distributed Execution Engine
...C++ FrontendPython Frontend

Android iOS ...GPUCPU
TensorFlow Distributed Execution Engine
...C++ FrontendPython Frontend
Layers Build models

Android iOS ...GPUCPU
TensorFlow Distributed Execution Engine
...C++ FrontendPython Frontend
Layers
Estimator
Keras
Model
Train and evaluate
models
Build models

model = Sequential()
model.add(Dense(512, input_shape=(vocab_size,), activation='relu'))
Building our model [ 0 0 0 1 ... 0 0 0 0 1 0 ]

model = Sequential()
model.add(Dense(512, input_shape=(vocab_size,), activation='relu'))
model.add(Dense(num_classes, activation='softmax'))
Building our model
[ 0.01 0.01 0.95 0.03 0.02 ]
[ 0 0 0 1 ... 0 0 0 0 1 0 ]

model = Sequential()
model.add(Dense(512, input_shape=(vocab_size,), activation='relu'))
model.add(Dense(num_classes, activation='softmax'))
model.compile(loss='categorical_crossentropy',
optimizer='adam',
metrics=['accuracy'])
Building our model
[ 0.01 0.01 0.95 0.03 0.02 ]
[ 0 0 0 1 ... 0 0 0 0 1 0 ]

4. Training
Surely machine learning is more than this

Test and
update model
Training Data
Model Prediction

model.fit(x_train, y_train,
batch_size=batch_size,
epochs=epochs)
Train the model

5. Evaluation
Always good to check your work

Evaluation Data
PredictionModel
Calculate
accuracy

model.fit(x_train, y_train,
batch_size=batch_size,
epochs=epochs)
model.evaluate(x_test, y_test,
batch_size=batch_size)
Train the model

6. Hyperparameter Tuning
Adjusting model parameters

Training Data
Prediction
Model
Model
Model
Model
Test and
update model

7. Prediction
The reason we did all that work

Cloud Machine Learning Engine
No-ops distributed TF training
Efficient, parallelized,
hyperparameter search
Serve TensorFlow, XGBoost,
and scikit-learn models in
production

bit.ly/kaggle-bbc-code
tensorflow.org
developers.google.com/machine-learning/crash-course
cloud.google.com/ml
What's Next?

Thank you
Yufeng Guo
tensorflow.org
cloud.google.com/ml
bit.ly/aiadventures
Helpful resources
@YufengG
@yufengg

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