Upgrade to Pro
— share decks privately, control downloads, hide ads and more …
Speaker Deck
Features
Speaker Deck
PRO
Sign in
Sign up for free
Search
Search
Who's Afraid Of Machine Learning? & first steps...
Search
Britt Barak
April 23, 2018
Technology
5
900
Who's Afraid Of Machine Learning? & first steps with TensorFlow
Chicago Roboto & Android Makers 2018
Britt Barak
April 23, 2018
Tweet
Share
More Decks by Britt Barak
See All by Britt Barak
[Vonage] Introducing Conversations
brittbarak
1
130
Kids, Play Nice! Kotlin-Java Interop In Mind
brittbarak
2
450
Sharing is Caring- Getting Started with Kotlin Multiplatform
brittbarak
2
2.1k
Between JOMO and FOMO: You are reshaping communication.
brittbarak
2
1.3k
Build Apps For The Ones You Love
brittbarak
1
120
What an ML-ful World! MLKit for Android dev.
brittbarak
0
130
Make your app dance with MotionLayout
brittbarak
8
1.4k
Who's afraid of ML? V2 : First steps with MlKit
brittbarak
1
460
Oh, the places you'll go! Cracking Navigation on Android
brittbarak
0
480
Other Decks in Technology
See All in Technology
Pythonによる契約プログラミング入門 / PyCon JP 2025
7pairs
5
2.4k
DataOpsNight#8_Terragruntを用いたスケーラブルなSnowflakeインフラ管理
roki18d
1
320
Goを使ってTDDを体験しよう!
chiroruxx
1
270
From Prompt to Product @ How to Web 2025, Bucharest, Romania
janwerner
0
110
LLMアプリケーション開発におけるセキュリティリスクと対策 / LLM Application Security
flatt_security
7
1.8k
Escaping_the_Kraken_-_October_2025.pdf
mdalmijn
0
110
多野優介
tanoyusuke
1
200
生成AIで「お客様の声」を ストーリーに変える 新潮流「Generative ETL」
ishikawa_satoru
1
280
What is BigQuery?
aizack_harks
0
120
KAGのLT会 #8 - 東京リージョンでGAしたAmazon Q in QuickSightを使って、報告用の資料を作ってみた
0air
0
200
GA technologiesでのAI-Readyの取り組み@DataOps Night
yuto16
0
260
職種別ミートアップで社内から盛り上げる アウトプット文化の醸成と関係強化/ #DevRelKaigi
nishiuma
2
130
Featured
See All Featured
Fantastic passwords and where to find them - at NoRuKo
philnash
52
3.4k
XXLCSS - How to scale CSS and keep your sanity
sugarenia
248
1.3M
Optimizing for Happiness
mojombo
379
70k
Building Better People: How to give real-time feedback that sticks.
wjessup
368
20k
Into the Great Unknown - MozCon
thekraken
40
2.1k
Mobile First: as difficult as doing things right
swwweet
224
9.9k
Automating Front-end Workflow
addyosmani
1371
200k
CoffeeScript is Beautiful & I Never Want to Write Plain JavaScript Again
sstephenson
162
15k
Why You Should Never Use an ORM
jnunemaker
PRO
59
9.5k
A better future with KSS
kneath
239
17k
Producing Creativity
orderedlist
PRO
347
40k
The Power of CSS Pseudo Elements
geoffreycrofte
79
6k
Transcript
Who’s afraid of Machine Learning? Britt Barak
Britt Barak Google Developer Expert - Android Women Techmakers Israel
Britt Barak @brittBarak
None
None
None
None
None
None
None
None
In a machine...
None
Strawberry Not Strawberry
Input Red Seeds pattern Top leaves 0.64 0.75 0.4
0.64 0.75 0.4 Input Red Seeds pattern Top leaves
0.64 0.75 0.4 Input Red Seeds pattern Top leaves
0.64 0.75 0.4 Input Red Seeds pattern Top leaves
0.64 0.75 0.4 Input 0.5 0.8 0.3 Red Seeds pattern
Top leaves
0.64 0.75 0.4 Input Red Seeds pattern Top leaves 0.5
0.8 0.3 0.5 * 0.64 + 0.8 * 0.75 + 0.3 * 0.4
0.64 0.75 0.4 Input Red Seeds pattern Top leaves 0.5
0.8 0.3 0.5 * 0.64 + 0.8 * 0.75 + 0.3 * 0.4 ___________ 1.04
0.64 0.75 0.4 Input Red Seeds pattern Top leaves 0.5
0.8 0.3 0.5 * 0.64 + 0.8 * 0.75 + 0.3 * 0.4 ___________ 1.04 + 0.7
0.64 0.75 0.4 1.74 0.5 * 0.64 + 0.8 *
0.75 + 0.3 * 0.4 ___________ 1.04 + 0.7 ___________ 1.74 Input Red Seeds pattern Top leaves 0.5 0.8 0.3
0.64 0.75 0.4 1.02 1.74 Input Red Seeds pattern Top
leaves 0.97
0.64 0.75 0.4 Input Red Seeds pattern Top leaves 1.02
1.74 0.97
0.64 0.75 0.4 Output Strawberry Not Strawberry Input Red Seeds
pattern Top leaves 1.02 1.74 0.97 0.87 0.13
0.64 0.75 0.4 0.87 0.13 Strawberry Not Strawberry Output Input
Red Seeds pattern Top leaves 1.02 1.74 0.97
None
0.7 0.03 0.01 3.72 0.89 1.92 Strawberry Not Strawberry Output
Input Red Seeds pattern Top leaves 0.2 0.8
0.7 0.03 0.01 3.72 0.89 1.92 Strawberry Not Strawberry Output
Input Red Seeds pattern Top leaves 0.2 0.8
0.7 0.03 0.01 3.72 0.89 1.92 0.2 0.8 Strawberry Not
Strawberry Output Input Red Seeds pattern Top leaves
0.5 * 0.64 + 0.8 * 0.75 + 0.3 *
0.4 ___________ 1.04 + 0.7 ___________ 1.74 Strawberry Not Not Strawberry Not Not Strawberry Not Not
Training TRAINING
0.64 0.75 0.4 1.02 1.74 0.97 0.89 0.11 Strawberry Not
Strawberry Output Input Red Seeds pattern Top leaves
Strawberry Not Strawberry Output Input Hidden Red Seeds pattern Top
leaves
None
Data science
We get a trained model !
TensorFlow - Open source - Widely used - Flexible for
scale: - 1 or more CPUs / GPUs - desktop, server, mobile device
Strawberry
Strawberry
Strawberry • Bandwidth • Performance • Latency • Network •
Security • Privacy • …
TensorFlow Mobile - Speech Recognition - Image Recognition - Object
Localization - Gesture Recognition - Translation - Text Classification - Voice Synthesis
Lightweight Fast Cross platform
MobileNet Inception-V3 SmartReply Models
None
Image Classifier classifier .classify(bitmap) label
1. Add Assets
None
labels.txt strawberry orange lemon fig pineapple banana jackfruit custard apple
pomegranate hay carbonara chocolate sauce dough meat loaf
2. Add TensorFlow Lite
repositories { maven { url 'https://google.bintray.com/tensorflow' } } dependencies
{ // ... implementation 'org.tensorflow:tensorflow-lite:+' } build.gradle
android { aaptOptions { noCompress "tflite" } } build.gradle
3. Create ImageClassifier.java
Image Classifier
ImageClassifier.java model = loadModelFile(); tflite = new Interpreter();
ImageClassifier.java model = loadModelFile(); tflite = new Interpreter(model);
MappedByteBuffer loadModelFile() { AssetFileDescriptor descriptor= getAssets().openFd(MODEL_PATH);
MappedByteBuffer loadModelFile() { AssetFileDescriptor descriptor= getAssets().openFd(MODEL_PATH); FileInputStream inputStream = new
FileInputStream(descriptor.getFileDescriptor()); FileChannel channel = inputStream.getChannel();
MappedByteBuffer loadModelFile() { AssetFileDescriptor descriptor= getAssets().openFd(MODEL_PATH); FileInputStream inputStream = new
FileInputStream(descriptor.getFileDescriptor()); FileChannel channel = inputStream.getChannel(); long start = descriptor.getStartOffset(); long length = descriptor.getDeclaredLength(); return channel.map(FileChannel.MapMode.READ_ONLY, start, length); }
Image Classifier [strawberry, apple, ... ] labels.txt
ImageClassifier.java model = loadModelFile(); tflite = new Interpreter(model); labelList =
loadLabelList();
labels.txt strawberry orange lemon fig pineapple banana jackfruit custard apple
pomegranate hay carbonara chocolate sauce dough meat loaf
List<String> loadLabelList() throws IOException { InputStreamReader inputStream = new InputStreamReader(getAssets().open(LABEL_PATH));
}
List<String> loadLabelList() throws IOException { InputStreamReader inputStream = new InputStreamReader(getAssets().open(LABEL_PATH));
BufferedReader reader = new BufferedReader(inputStream); }
List<String> loadLabelList() throws IOException { InputStreamReader inputStream = new InputStreamReader(getAssets().open(LABEL_PATH));
BufferedReader reader = new BufferedReader(inputStream); List<String> labelList = new ArrayList<>(); String line; while ((line = reader.readLine()) != null) { labelList.add(line); } }
List<String> loadLabelList() throws IOException { InputStreamReader inputStream = new InputStreamReader(getAssets().open(LABEL_PATH));
BufferedReader reader = new BufferedReader(inputStream); List<String> labelList = new ArrayList<>(); String line; while ((line = reader.readLine()) != null) { labelList.add(line); } reader.close(); return labelList; }
Image Classifier [ [0..6] , [ 0.1 ] , ...
] [strawberry, apple, ... ] probArray labels.txt
probArray = { [0.7], [0.3], [0], [0], } labelList =
{ strawberry, apple, pineapple, banana, } 0.3
ImageClassifier.java model = loadModelFile(); tflite = new Interpreter(model); labelList =
loadLabelList(); probArray = new float[1][labelList.size()];
Image Classifier [......] [ [0..6] , [ 0.1 ] ,
... ] [strawberry, apple, ... ] ByteBuffer probArray labels.txt
ImageClassifier.java model = loadModelFile(); tflite = new Interpreter(model); labelList =
loadLabelList(); probArray = new float[1][labelList.size()]; imgData = ByteBuffer.allocateDirect( DIM_IMG_SIZE_X * DIM_IMG_SIZE_Y * DIM_PIXEL_SIZE); imgData.order(ByteOrder.nativeOrder());
4. Run the model / classify
classifier .classify(bitmap) Image Classifier [......] [ [0..6] , [ 0.1
] , ... ] [strawberry, apple, ... ] ByteBuffer probArray labels.txt
ImageClassifier.java String classify(Bitmap bitmap) { convertBitmapToByteBuffer(imgData, bitmap);
}
void convertBitmapToByteBuffer(Bitmap bitmap) { //... bitmap.getPixels(intValues, 0, bitmap.getWidth(), 0, 0,bitmap.getWidth(),
bitmap.getHeight()); }
void convertBitmapToByteBuffer(Bitmap bitmap) { //... bitmap.getPixels(intValues, 0, bitmap.getWidth(), 0, 0,bitmap.getWidth(),
bitmap.getHeight()); int pixel = 0; for (int i = 0; i < DIM_IMG_SIZE_X; ++i) { for (int j = 0; j < DIM_IMG_SIZE_Y; ++j) { final int val = intValues[pixel++]; imgData.put((byte) ((val >> 16) & 0xFF)); imgData.put((byte) ((val >> 8) & 0xFF)); imgData.put((byte) (val & 0xFF)); } } }
void convertBitmapToByteBuffer(Bitmap bitmap) { //... bitmap.getPixels(intValues, 0, bitmap.getWidth(), 0, 0,bitmap.getWidth(),
bitmap.getHeight()); int pixel = 0; for (int i = 0; i < DIM_IMG_SIZE_X; ++i) { for (int j = 0; j < DIM_IMG_SIZE_Y; ++j) { final int val = intValues[pixel++]; imgData.put((byte) ((val >> 16) & 0xFF)); imgData.put((byte) ((val >> 8) & 0xFF)); imgData.put((byte) (val & 0xFF)); } } }
ImageClassifier.java String classify(Bitmap bitmap) { convertBitmapToByteBuffer(imgData, bitmap); tflite.run(imgData,
probArray); }
ImageClassifier.java String classify(Bitmap bitmap) { convertBitmapToByteBuffer(imgData, bitmap); tflite.run(imgData,
probArray); String textToShow = getTopLabels(); return textToShow; }
Strawberry - 0.87 Apple - 0.13 Tomato - 0.01
Machine Learning is a new world
Links - Tensorflow - https://www.tensorflow.org/ - Tensorflow lite - https://www.tensorflow.org/mobile/tflite/
- Codes labs - codelabs.developers.google.com/codelabs/tensorflow-for-poets-2-tflite/ - Google’s Machine Learning Crash Course - developers.google.com/machine-learning/crash-course/ - [Dr. Joe Dispenza]
Thank you! Keep in touch! Britt Barak @brittBarak