Getting started with machine learning using cloud computing isn't as hard as you may have thought. With Google Cloud Platform you've ready to use, state of art APIs full of intelligence such as cloud vision for image processing, cloud speech for sound recognition / transcription, natural language for text analysis, and of course cloud translate for language detection and translation.

1. How Can You Get Started
with Machine Learning?
Abdelrahman Omran

2. How Can You Get Started with Machine Learning?
Three ways, with varying complexity:
(1) Use a Cloud-based API (Vision, Natural Language, etc.)
(2) Use existing model architecture, and retrain it on your dataset.
(3) Develop your own machine learning models for new problems.
More
flexible,
but more
effort
required

3. Cloud Machine Learning APIs
See, Hear and Understand the world

4. Cloud
Natural Language
Cloud
Speech
Cloud
Translate
Cloud
Vision

5. 55
Vision API Demo

6. Faces
Faces, facial landmarks, emotions
OCR
Read and extract text, with
support for > 10 languages
Label
Detect entities from furniture to
transportation
Logos
Identify product logos
Landmarks & Image Properties
Detect landmarks & dominant
color of image
Safe Search
Detect explicit content - adult,
violent, medical and spoof
Cloud Vision API

7. API Usage: Detect Objects in an Image
Image Detected
Items
Vision API
Create JSON
request with the
image or pointer
to an image
Process
the JSON
response
Call the
REST API1 2 3

8. Confidential & ProprietaryGoogle Cloud Platform 8
Cloud Natural Language API
Extract sentence, identify parts of
speech and create dependency parse
trees for each sentence.
Identify entities and label by types such
as person, organization, location, events,
products and media.
Understand the overall sentiment of a
block of text.
Syntax Analysis Entity Recognition
Sentiment Analysis

9. 99
Natural Language API Demo

10. Confidential & ProprietaryGoogle Cloud Platform 10
Cloud Speech API
Automatic Speech Recognition
(ASR) powered by deep learning
neural networking to power your
applications like voice search or
speech transcription.
Recognizes over 80
languages and variants
with an extensive
vocabulary.
Returns partial
recognition results
immediately, as they
become available.
Filter inappropriate
content in text results.
Audio input can be captured by an application’s
microphone or sent from a pre-recorded audio
file. Multiple audio file formats are supported,
including FLAC, AMR, PCMU and linear-16.
Handles noisy audio from many
environments without requiring
additional noise cancellation.
Audio files can be uploaded in the
request and, in future releases,
integrated with Google Cloud
Storage.
Automatic Speech Recognition Global Vocabulary Inappropriate Content
Filtering
Streaming Recognition
Real-time or Buffered Audio Support Noisy Audio Handling Integrated API

11. 1111
Show me the code..

12. Go Practice Now..
Codelabs
codelabs.developers.google.com/codelabs/cloud-vision-intro/index.html
codelabs.developers.google.com/codelabs/cloud-speech-intro/index.html
codelabs.developers.google.com/codelabs/cloud-nl-intro/index.html
For Developers
cloud.google.com/vision/
cloud.google.com/speech/
cloud.google.com/natural-language/
cloud.google.com/translate/
Stack Overflow

13. Questions?

14. What’s Next?
tensorflow.org Want to learn more?
cloud.google.com Udacity class on Deep Learning, goo.gl/iHssII
github.com/tensorflow
googlecloudplatform.github.io
github.com/GoogleCloudPlatform
Guides, codelabs, videos
MNIST for Beginners, goo.gl/tx8R2b
TF Learn Quickstart, goo.gl/uiefRn
TensorFlow for Poets, goo.gl/bVjFIL
ML Recipes, goo.gl/KewA03
TensorFlow and Deep Learning without a PhD, goo.gl/pHeXe7

15. Thanks and have fun!
Abdelrahman Omran
me@omranic.com
@Omranic