Presentation slides at HTML5 conference 2016 Tokyo.

1. Google confidential | Do not distribute
Machine Learning Basics
for Web Application Developers
Etsuji Nakai
Cloud Solutions Architect at Google
2016/08/19 ver1.2

2. $ who am i
▪Etsuji Nakai
Cloud Solutions Architect at Google
Twitter @enakai00

3. Machine Learning Basics

4. Linear Binary Classifier
▪ Build a model to classify two types of
data with a straight line.
●
The model will predict the probability of
being in the positive class for new data.
●
It’s like predicting if the patient is
infected with a specific virus based on the
preliminary check result.
▪ Observe how the model is trained on
“Neural Network Playground”
●
http://goo.gl/A2G4Hv
x : Positive
o : Negative

5. Logistic Regression
▪ The straight line can be represented
as below, which can be translated to a
probability through the logistic
function σ.
▪ “To train the model” is to adjust the
parameters so that the
model fits in the training dataset.
Logistic function σ
Probability of
being positive
The value of f increases
in this direction

6. How to measure “fitness” of the model
▪ You define the “loss function” which indicates the non-fitness of the model. Then
ML algorithms adjust parameters to minimize the loss function.
●
In logistic regression, you adjust the parameters to maximize the probability of giving a
perfect prediction for the training dataset.
●
For example, suppose that n-th data is given as and its correct label is
(1=x, 0=o). Then the probability that the model gives the correct prediction for this
data is:
●
Hence the probability of giving correct predictions for all data is:
●
By defining the loss function E as below, you cal tell ML algorithms to minimize it.

7. Graphical Understanding of Linear Classifier
▪ Drawing 3-dimensional graph of ,
you can see that the “tilted flat plane”
divides the plane into two classes.

8. Linear Multiclass Classifier (Hardmax)
▪ How can you divide the plane into
three classes (instead of two)?
▪ You can define three liner functions and
classify the point based on “which of them
has the maximum value at that point.”
●
It is equivalent to dividing with the three
tilted flat planes.

9. Linear Multiclass Classifier (Softmax)
▪ You can define the probability that
belongs to the i-th class as below:
▪ This translates the magnitude of
into the probability satisfying the following
conditions.
One dimensional example of
the softmax translation.

10. Image Classification
with Neural Network

11. Classifying Images with Softmax function
▪ For example, a gray scale image with 28x28
pixels can be represented as a 784 dimensional
vector. (i.e a collection of 784 float numbers.)
●
In other word, it corresponds to a single point in a
784 dimensional space!
▪ When you spread a bunch of images into this 784
dimensional space, similar images may come
together to form clusters of images.
●
If this is a correct assumption, you can classify the
images by dividing the 784 dimensional space with
the softmax function.

12. Let’s try with TensorFlow Correct Incorrect
http://goo.gl/rGqjYh
▪ You can see the code and its result (92% accuracy).
* Comments are in Japanese.

13. Improving Accuracy using CNN
Raw
Image
Softmax Function
Pooling
Layer
Convolution
Filter
・・・
Convolution
Filter
・・・
・・・
Dropout Layer
Fully-connected Layer
Pooling
Layer
Convolution
Filter
・・・
Convolution
Filter
Pooling
Layer
・・・
Pooling
Layer
▪ Instead of providing the raw image data into the softmax
function, you can extract “features” of images through
convolutional filters and pooling layers.

14. Let’s try with TensorFlow
http://goo.gl/UHsVmI
http://goo.gl/VE2ISf
▪ You can see the code and its result (99.2% accuracy).

15. A new Book for TensorFlow and CNN!
https://www.amazon.co.jp/dp/4839960887/
* This is available only in Japanese now. Please ask publishers in your region to make a translation ;)

16. Client Applications using ML models

17. API services for pre-trained models
http://goo.gl/dh6cwB
▪ See an example.
Send Image
Client
Cloud Service
Reply the location of faces
and their emotions.

18. Smile Detection from Webcam Images
▪ The browser code sends webcam images to Google Vision API
and notify when you’re smiling ;)
http://goo.gl/9EM8tr

19. Cucumber Classification with TensorFlow
▪ A cucumber farmer built an original “Cucumber
sorter” using TensorFlow.
▪ Client application running on RasPi works with
the Aruduino Micro to control the belt conveyor
and the sorting devices.
https://cloud.google.com/blog/big-data/2016/08/how-a-japanese-cucumber-farmer-is-using-deep-learning-and-tensorflow

20. Other Possible Architectures
▪ Providing additional data to a pre-trained model to fine-tune it for your
specific purpose.
●
Technically referred as “Transfer Learning.”
▪ Running trained model on the client.
●
You need a lot of computing resource to train the model. But you can use the
trained model directly on the client.
▪ Realtime model training on the client?
●
Considering the increasing computing resource available on the client, you may be
able to train the model dynamically on the client using realtime data (such as
webcam images) available on the client.

21. Similarity between model training and application development
Revised Model
Additional
Data
Revised Model
Final Model
Applications
API
access
Training
Production environment
Test
Test
Upgrade models
Training
Fix and retry
success
fail
Existing
Models
Version control
of models
Model tunings
Preprocess and feed
Deploy new
models
▪ This resembles the software development model (CI/CD).
▪ There will be some de-fact tools to build this framework
in near future (maybe.)
Existing
Models
Existing
Models

22. Thank you!