This document introduces TensorFlow, an open-source library for machine learning. It outlines the basic workflow of training a model using labeled data, including epochs and model adjustment. Additionally, it provides practical steps for applying TensorFlow to real-world problems.

1. Getting started with
TensorFlow
Gustavo García
@anarchyco

2. Machine Learning is everywhere

4. TensorFlow is an open source library for numerical
computation, specializing in machine learning
applications

5. TensorFlow Hello World

6. Machine Learning Pseudocode
labeled_data = ... // f.e. X: square_meters -> y: house_prices
model = ... // f.e. y = B * x + A
// Training
do N times: // epochs
for d in labeled_data: // usually in batches
y_predicted = model.predict(d)
model.adjust(y_predicted - y, learning_rate)
// Inference
model.predict(unseen_data)

8. Deep Neural Network with TensorFlow

9. Deep Neural Network with TensorFlow

10. Deep Neural Network with TensorFlow

11. Deep Neural Network with TensorFlow

12. Deep Neural Network with TensorFlow

13. Deep Neural Network with TensorFlow

19. Real world TensorFlow
1. Find a model to the type of problem you want to solve
2. Obtain as much good data as possible
3. Train the model (or retrain)
4. Save the model and deploy it

21. Demo

22. The End