The document introduces Keras, a high-level neural networks API written in Python designed for easy and fast prototyping. It emphasizes user experience by offering a simple API, minimizing cognitive load, and providing clear feedback on errors. The document also includes examples of different neural network models and resources for further learning.

1. Intro to Keras
John Ramey
@ramhiser

2. About Me
• Principal Data Scientist @
Novi Labs
• Stats + CS Background
• Soccer!

3. My Goal Tonight
• Introduce you to Keras
• Not teach you all of Keras
• But make damn sure that you can use Keras

4. Keras: Deep Learning Library
• High-level neural networks API, written in Python
• Models described in Python code. No separate models
config files
• Allows for easy and fast prototyping (through user
friendliness, modularity, and extensibility)
• Runs seamlessly on CPUs and GPUs

5. Keras: Backends
•TensorFlow, Theano, CNTK (Microsoft)
•Can deploy in production via TensorFlow Serving

6. Source: https://keras.io/#guiding-principles
Keras is an API designed for human beings, not
machines. It puts user experience front and center.
Keras follows best practices for reducing cognitive
load: it offers consistent & simple APIs, it minimizes
the number of user actions required for common use
cases, and it provides clear and actionable feedback
upon user error.

7. Really. That’s it.
pip install keras
How to Install Keras

8. import tensorflow.contrib.keras
Another Way
•Keras was rebuilt from the ground up for better TF integration
•Can take advantage of TF’s power and flexibility
•Will be import tensorflow.keras soon (TF 1.3?)

9. Also, Docker

10. Demo: TensorFlow +
Keras Docker Image
https://github.com/ramhiser/Keras-Tutorials

11. MNIST: The Hello, World of
Deep Learning

12. MNIST: The Hello, World of
Deep Learning

13. MNIST: Training
( , 2), ( , 8)
…

14. MNIST: Test
( , ?)

15. 1st Model: Logistic
Regression
Source: https://stackoverflow.com/q/40537503/234233
Features Weights
2
Target
Goal: Learn “best” weights

16. 1st Model: Logistic
Regression
Source: https://stackoverflow.com/q/40537503/234233
Features Weights
2
Target
Activation Function (nonlinear)

17. Demo
https://github.com/ramhiser/Keras-
Tutorials/blob/master/notebooks/02_logistic_regression.ipynb

18. 2nd Model: Multilayer
Perceptron
2W11
W21
W1p W2p
Features 1st Weights 2nd Weights Target

19. Demo
https://github.com/ramhiser/Keras-
Tutorials/blob/master/notebooks/03_multilayer_perceptron.ipynb

20. 3rd Model: Convolutional
Neural Net

21. Convolution
Image
Filter
Matrix
Stride = 1
Pad = 0

22. Convolution
Source: https://ujjwalkarn.me/2016/08/11/intuitive-explanation-
convnets/

23. Max Pooling
Source: http://www.wildml.com/2015/11/understanding-convolutional-
neural-networks-for-nlp/

24. Demo
https://github.com/ramhiser/Keras-
Tutorials/blob/master/notebooks/05_convolutional_neural_net.ipynb

25. Loads More Examples
Source: https://github.com/fchollet/keras/tree/master/examples

26. Lots of Pre-Trained Models
Source:
https://github.com/fchollet/keras/tree/master/keras/applications

27. Inception (v3)

28. Check This Out

32. Thank You!