TensorFlow in 3 sentences Barbara Fusinska provides a high-level overview of TensorFlow in 3 sentences or less. She demonstrates how to build a computational graph for classification tasks using APIs like tf.nn and tf.layers. Barbara encourages attendees to get involved with open source TensorFlow communities on GitHub and through tools like Docker containers.

1. TensorFlow in 3 sentences
Barbara Fusinska
@BasiaFusinska

2. About me
Data Science Freelancer
Machine Learning
Programmer
@BasiaFusinska
BarbaraFusinska.com
Barbara@Fusinska.com

3. Agenda
• What is Deep Learning?
• MNIST dataset Classification
• 3 sentences & ~6 demos
https://katacoda.com/basiafusinska/courses/tensorflow-in-3-sentences

4. Deep Learning?

6. Neural
Networks
• Interconnected units (neurons)
• Activation signal(s)
• Information processing
• Learning involves adjustments
to the synaptic connections

7. Artificial Neural Networks building blocks

8. Brief History of Neural Network

9. What has
changed?
• Big Data
• The Cloud
• GPU
• Training methods
• Tools
• Computer Vision, NLP

11. Build the Computational Graph
Sentence #1

12. What is Computational Graph?

13. Computational Graph: Constants,
Placeholders & Variables
Demo

14. Classification task
Network training
Data & Labels
0
1
2
3
4
5
6
7
8
9http://yann.lecun.com/exdb/mnist/

15. Data preparation
28 x 28
784

16. Training process
Batches
Dataset
Parameters adjusting
Loss function
minimisation

17. Using trained model
784
Input data
...
Input layer
2
9
0
1
...
Output layer
𝑜 = 𝜑(𝑾 ∙ 𝑥 + 𝒃)
arg max
9
Label

18. Computational Graph: Training
process
Demo

19. Use APIs
Sentence #2

20. Machine Learning workflow
Data
preparation &
clean
Data split
Fit Machine
Learning
Coefficients Trained model Evaluate
Training
data
Test data

22. Hidden layer
...
Hidden layer
2
9
0
1
...
Output layer
...
Input layer
ℎ = 𝑅𝑒𝐿𝑈(𝑾 ∙ 𝑥 + 𝒃)

23. APIs: Estimators
Demo

24. Convolutional
Neural Network
• Inputs have higher
dimensions
• Reduce the number of
parameters (W, b)
• Neurons arranged in 3D
• Connected to the region
of the previous layer

25. Pooling
• Reduce spatial size
• Control overfitting
• Applied to every depth slice
• Window size, stride
• Max, Average

26. Convolutional layers
...
Dense layer
2
9
0
1
...
Output layer
...
Flattened convolutionInput layer 1st Convolutional 2nd Convolutional

27. APIs: tf.nn
Demo

28. APIs: tf.layers
Demo

29. Work with the
communities
Sentence #3

30. OpenSource

32. https://www.tensorflow.org/tutorials/seq2seq

34. Communities: Inception & Docker
Demo

36. Keep in touch
BarbaraFusinska.com
Barbara@Fusinska.com
@BasiaFusinska
https://katacoda.com/basiafusinska/courses/tensorflow-in-3-sentences