how can you get started with machine learning?€¦ · how can you get started with machine...

Copyright 2017 Arbormoon Software, Inc. All rights reserved.

How Can You Get Started with Machine Learning?

Harry Wang


Presentation.introduceSpeaker()


-Subset of Artificial Intelligence

-Algorithms that can adjust themselves based on data

-Statistical and Predictive Analytics

-”Gives computers the ability to learn without being explicitly programmed”

-More flexible and less-static in behavior

What is Machine Learning?


Supervised Learning

InputInput


Unsupervised Learning


Semi-Supervised Learning


Classifier - object in program that learns from the data you give it

Training Data - used to train classifier

Testing Data - used to evaluate the accuracy and performance of classifier

Basic Machine Learning concepts


Example of a Simple ML Modely = m’x+b’y = mx+b

Initial State

Final State


Examples of Machine Learning


spotted salamander fire salamander retriever

Recognizing images with Inception

https://research.googleblog.com/2016/03/train-your-own-image-classifier-with.html


Smart reply in Inbox by Gmail

10%of all responses sent on mobile


[glacier]

Google Photos


How Can You Get Started with Machine Learning?

More flexible, but more

effort required

Three ways, with varying complexity:

(1) Use a Cloud-based or Mobile API (Vision, Natural Language, etc.)

(2) Use an existing model architecture, and retrain it or fine tune on your dataset

(3) Develop your own machine learning models for new problems


Cloud Machine Learning APIs See, Hear and Understand the world


Cloud Natural Language

Cloud Speech

Cloud Translate

Cloud Vision

Copyright 2017 Arbormoon Software, Inc. All rights reserved.19

Vision API Demo


FacesFaces, facial landmarks, emotions

OCRRead and extract text, with support for > 10 languages

LabelDetect entities from furniture to transportation

LogosIdentify product logos

Landmarks & Image PropertiesDetect landmarks & dominant color of image

Safe SearchDetect explicit content - adult, violent, medical and spoof

Cloud Vision API


API Usage: Detect Objects in an Image

Image DetectedItems

Vision API

Create JSON request with the image or pointer to an image

Process the JSON response

Call the REST API1 2 3


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


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


What’s Next?

Codelabscodelabs.developers.google.com/codelabs/cloud-vision-intro/index.htmlcodelabs.developers.google.com/codelabs/cloud-speech-intro/index.htmlcodelabs.developers.google.com/codelabs/cloud-nl-intro/index.html

For Developerscloud.google.com/vision/cloud.google.com/speech/cloud.google.com/natural-language/cloud.google.com/translate/

Stack Overflow


Mobile Vision APIProviding on-device vision for applications


Face APIfaces, facial landmarks, eyes open, smiling

Barcode API1D and 2D barcodes

Text APILatin-based text / structure

Common Mobile Vision APISupport for fast image and video on-device detection and tracking.


Googly Eyes Android App

Video credit Google

1. Create a face detector for facial landmarks (e.g., eyes)

3. For each face, draw the eyes

FaceDetector detector = new FaceDetector.Builder().setLandmarkType(FaceDetector.ALL_LANDMARKS).build();

SparseArray<Face> faces = detector.detect(image);

for (int i = 0; i < faces.size(); ++i) {Face face = faces.valueAt(i);for (Landmark landmark : face.getLandmarks()) {// Draw eyes

2. Detect faces in the image


Face APIPhoto credit developers.google.com/vision


Text Structure

Blocks

Lines

Words

Lines

Words Words Words


Barcode Detection1D barcodes

EAN-13/8UPC-A/ECode-39/93/128

ITFCodabar

2D barcodes QR CodeData MatrixPDF-417AZTEC

UPC

DataMatrix

QR Code

PDF 417

Video and image credit Google


Combined Vision & Translation


Mobile Vision: Codelabs and Samples

Googly Eyes Code Samplegithub.com/googlesamples/android-vision/tree/master/visionSamples/googly-eyes

Codelabscodelabs.developers.google.com/codelabs/face-detection/codelabs.developers.google.com/codelabs/mobile-vision-ocr/

Mobile Vision Developersdevelopers.google.com/vision/

GitHub Code Samplesgithub.com/googlesamples/android-vision

Stack OverflowFind and ask questions under the android-vision tag.


Open source Machine Learning library

Especially useful forDeep Learning

For research and production

Apache 2.0 license


Hello World

Image from https://github.com/mnielsen/neural-networks-and-deep-learning

?


What we see What the computer “sees”


Complete code

import numpy as np

import tensorflow as tf

mnist = tf.contrib.learn.datasets.load_dataset('mnist')

data = mnist.train.images

labels = np.asarray(mnist.train.labels, dtype=np.int32)

data = data[:10000]

labels = labels[:10000]

feature_columns = tf.contrib.learn.infer_real_valued_columns_from_input(data)

classifier = tf.contrib.learn.LinearClassifier(feature_columns=feature_columns, n_classes=10)

classifier.fit(data, labels, batch_size=100, steps=1000)

print("accuracy: ", classifier.evaluate(mnist.test.images, np.asarray(mnist.test.labels, dtype=np.int32))["accuracy"])


Computer Vision Principles

Filled in Center:Positive Weight for digit being a one

Filled in Center:Negative Weight for digit being a zero


Computer Vision Principles


Getting Started Exercises


Lots of tutorials at tensorflow.org


Codelab - goo.gl/xGsB9d Video - goo.gl/B2zYWN

TensorFlow for Poets


Mobile TensorFlow


A little more TensorFlow


A multidimensional array.

A graph of operations.


Data Flow Graphs

Computation is defined as a directed acyclic graph (DAG) to optimize an objective functionGraph is defined in high-level language (Python)Graph is compiled and optimizedGraph is executed (in parts or fully) on available low level devices (CPU, GPU)Data (tensors) flow through the graphTensorFlow can compute gradients automatically


ArchitectureCore in C++

Different front ends

- Python and C++ today, community may add more

...

Core TensorFlow Execution System

CPU GPU Android iOS ...

C++ front end Python front end


Raspberry PiDatacentersYour laptop Android iOS

Portable & Scalable


/Serving

Open-source solution for serving trained models


tensorflow.orggithub.com/tensorflow

Want to learn more?Udacity class on Deep Learning, goo.gl/iHssII

Guides, codelabs, videosMNIST for Beginners, goo.gl/tx8R2bTF Learn Quickstart, goo.gl/uiefRnTensorFlow for Poets, goo.gl/bVjFILML Recipes, goo.gl/KewA03TensorFlow and Deep Learning without a PhD, goo.gl/pHeXe7

What's Next


Questions?


Thanks and have fun!

Harry Wang

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