Construir una App con AI nunca fue tan fácil

“Construir una App
con AI nunca fue tan fácil”
con frameworks y servicios que nos acercan la
potencia de la inteligencia artificial y el machine
learning
Rubén Chavarri Elduayen
Software Engineer en Kairos
@pekewake
David Chavarri Elduayen
Software Architect en BBVA
@dvdchavarri
MAD · NOV 24-25 · 2017

Rubén y David · Contruir una App con AI nunca fue tan facil · @pekewake @dvdchavarri
Introduction to Artificial Intelligence
In the next years, fullstack will
not be full stack without
AI skills
‘‘
’’

Type of
MachineLearning
algorithms

● Neurons
● Activation Functions
● Cost Functions
● Gradient Descent and Backpropagation
How Machine Learning works

● Neurons in ML called Perceptrons

● Activation Functions: method which calculate the output
ReLUTanhLinear Sigmoid

● Cost Functions: Method to evaluate the performance of
a neuron based on how far off we are for expected value.
○ Quadratic Cost
○ Cross Entropy

● Gradient Descent: Optimization
algorithm to minimize cost function.
● Backpropagation: Calculates the error
contribution of each neuron

http://playground.tensorflow.org

How MachineLearning can make our Apps smart?

Pinterest
Spam moderation
Content discovery
Advertising monetization
Reducing churn of email

Facebook
Chatbot Army
Filter out spam
Poor-quality content
Computer vision

Twitter
Curated Timelines.
Real time and “scores”.
Decisions based on your
individual preferences.
Deep learning models.

Spotify
Discovery Weekly
+ Collaborative Filtering
+ Natural Language Processing
+ Audio models

Shazam
Fingerprint for recording
Generative models

Google
DeepMind network,
the “machine that dreams”.
Natural language Processing.
Speech translation.

Where to start?
ML as a services vs My Neuronal Network

Best MLaaS? Source link
Amazon ML Google Cloud ML IBM Watson MS Azure
Overview Large automated platform that
applies ML algorithms to data
stored in AWS
State-of-the-art of Google algorithms
and other industry-leading apps.
Focused on production models
through REST API connectors
Long list of predefined algorithms but less
automated.
Interface Amazon ML Console
Amazon cmd line Interface
Google Cloud cmd line interface
integrated with TensorFlow
IBM graphical analysis
API connectors enable users to
build 3rd data science apps
Azure ML Studio drag-and-drop env.
R & Python packages
Algorithms Regression, Classification, multi
class classification
Video, Image & Text Analysis, Speech
recognitions, Translation
Links to Apache Spark’s MLib
IBM Data Science workbench
Scale boosted decision tree
Bayesian recommendation sys.
Multicass & binary classification
Regression clustering
Suggestions Yes Yes No No
Pricing $0.42/h Data analysis y model
$0,10 per thousand predictions
$0.49/h Model Training
$0.10 per thousand predictions
Free tier for 5k predictions & 5h
$0.45 computer hour
$0.50 per thousands predics
Limited free tier for devs
$0.99 user/month
$1.00 Studio experiment hour
Extras Data Storage extra fees Google account required IBM SPSS Modeler or Data
Science experience required.
Bluemix account required
Azure or Microsoft account required
Others Automatic data transf. tool Little abstraction. Steep learning curve Focus on Backend ML with API Visual interface, limited insight

Machine Learning hands on - FiendlyChat
https://codelabs.developers.google.com/codelabs/firebase-cloud-functions/
Google Codelab
Cloud function & AI Moderation
+

Machine Learning hands on - FiendlyChat diagram
Safe
Detection
Blur image
when violent
Write
Location
Push

https://cloud.google.com/vision/docs/drag-and-drop

Show me the Code - ML Api Call
exports.blurOffensiveImages = functions.storage.object().onChange(event => {
const object = event.data;
const image = { source: {imageUri: `gs://${object.bucket}/${object.name}`} };
// Check the image content using the Cloud Vision API.
return vision.safeSearchDetection(image).then(batchAnnotateImagesResponse => {
const safeSearchResult = batchAnnotateImagesResponse[0].safeSearchAnnotation;
const Likelihood = Vision.types.Likelihood;
if (Likelihood[safeSearchResult.adult] >= Likelihood.LIKELY ||
Likelihood[safeSearchResult.violence] >= Likelihood.LIKELY) {
console.log('The image', object.name, 'has been detected as inappropriate.');
return blurImage(object.name, object.bucket);
} else {
console.log('The image', object.name,'has been detected as OK.');
}
});
});

Machine Learning hands on - FiendlyChat Code

(2015-2016)

My first neuronal network - TensorDraw
Repo: https://github.com/Twiinlab/codemotion2017 inspired by: https://quickdraw.withgoogle.com/
Main Tools
TensorFlow & App Engine
+

Python data science libraries
Numpy: Numerical processing
Pandas: Data Analysis
MatPlotLib: Visualization
SciKit: Processing machine learning data sets

pre-processing training evaluate
load / split / images CNN network check accuracy
model
TensorFlow
Training
save
bucle
Data source: https://quickdraw.withgoogle.com/data

Show me the Code - Training
# Imports libraries
# layers configuration
...
# Optimizer
...
# Loss Function
...
# image helper
fileList = ['smile','car','pencil','burger','moon','hand','tornado','scissors','mug','sock']
class SketchImageHelper():
# init training images
def set_up_images(self):
sampleSize = self.pos_end - self.pos_begin
for i in range(len(self.fileList)):
partialImages = np.array(np.load(fileList[i])[self.pos_begin:self.pos_end])
self.images.append( partialImages / 255)
self.labels.append(np.full((sampleSize,10), one_hot_encode(i)))
def next_batch(self, batch_size):
for i in range(len(self.fileList)):
x = np.array((self.images[i])[self.position:self.position+batch_size])
y = np.array((self.labels[i])[self.position:self.position+batch_size])
self.position = (self.position + batch_size)
return x, y

Show me the Code - Training
...
# init test images
sih = SketchImageHelper()
sih.set_up_images()
saver = tf.train.Saver()
with tf.Session() as sess:
steps = 3000
sess.run(init)
for j in range(steps):
batch_x , batch_y = sih.next_batch(100)
sess.run(train,feed_dict={x:batch_x,y_true:batch_y,hold_prob:0.5})
if j%50 == 0:
# Validate the Train Model
matches = tf.equal(tf.argmax(y_pred,1),tf.argmax(y_true,1))
acc = tf.reduce_mean(tf.cast(matches,tf.float32))
print(sess.run(acc,feed_dict={x:test_x,y_true:test_y,hold_prob:1.0}))
saver.save(sess, savefile)

Show me the Code - Network configuration

pre-processing
/prediction
restore
model
TensorFlow
Prediction
result : [ prob1,
prob2,
…
prob10]
APP
ENGINE
prediction
grey scale
resize image

Show me the Code - Prediction
sess = tf.InteractiveSession()
sess.run(tf.global_variables_initializer())
saver = tf.train.Saver()
saver.restore(sess, "./STORED_model/my_trained_model.json")
def readGrayImageFromData(data):
imageToPredict = rgba2rgb(data)
return rgb2grey(imageToPredict)
def simplifyImage(originalImage):
partialResizeImage = tform.resize(originalImage, (28, 28), order=5)
return (1 - np.reshape(partialResizeImage,newshape=(1,784)))
def predictFromDataImage(data):
imageData = readGrayImageFromData(data)
imageSimple = simplifyImage(imageData)
feed_dict = {x: imageSimple, y_true: np.zeros((1, 10)), hold_prob : 0.5 }
return sess.run(tf.nn.softmax(y_pred), feed_dict)

try here: https://tensorweb-af554.firebaseapp.com/

The Future of MachineLearning
What’s next in machine learning trends?
Machines That Learn More Effectively
Automation of Cyberattack Countermeasures
Convincing Generative Models
Better Machine Learning Training

Thanks!
Any question?
Rubén Chavarri Elduayen
@pekewake
David Chavarri elduayen
@dvdchavarri

Special Thanks to
Jorge del Val Juantomas García Gema Parreño Codemotion

RESOURCES:
Código fuente:
Demo: ML as a service
https://codelabs.developers.google.com/codelabs/firebase-web/
Demo: my neuronal network
https://github.com/Twiinlab/codemotion2017
Recomended course:
https://www.tensorflow.org/tutorials/
https://www.udemy.com/complete-guide-to-tensorflow-for-deep-learning-with-python/
AI - ML - DeepLearning
https://www.datascience.com/blog/machine-learning-and-deep-learning-what-is-the-difference
https://blogs.nvidia.com/blog/2016/07/29/whats-difference-artificial-intelligence-machine-learning-deep-learning-ai/
Real apps
http://www.wordstream.com/blog/ws/2017/07/28/machine-learning-applications
https://blog.twitter.com/engineering/en_us/topics/insights/2017/using-deep-learning-at-scale-in-twitters-timelines.html
https://hackernoon.com/spotifys-discover-weekly-how-machine-learning-finds-your-new-music-19a41ab76efe
https://www.toptal.com/algorithms/shazam-reconocimiento-de-algoritmos-de-m%C3%BAsica-huellas-dactilares-y-procesamiento/es
Machine Learning framework comparison
https://blog.paperspace.com/which-ml-framework-should-i-use/
https://project.inria.fr/deeplearning/files/2016/05/DLFrameworks.pdf

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Construir una App con AI nunca fue tan fácil