Machine learning, WTF!?
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O que é e como começar a usar Machine Learning!
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Machine learning, WTF!?
- 3. How Can You Get Started with Machine Learning? 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 More flexible, but more effort required
- 4. Cloud Machine Learning APIs See, Hear and Understand the world
- 7. 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
- 8. 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
- 9. Confidential & ProprietaryGoogle Cloud Platform 9 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
- 10. 1010 Natural Language API Demo
- 11. Confidential & ProprietaryGoogle Cloud Platform 11 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
- 12. 1212 Cloud Speech API Demo
- 14. Mobile Vision API Providing on-device vision for applications
- 15. Face API faces, facial landmarks, eyes open, smiling Barcode API 1D and 2D barcodes Text API Latin-based text / structure Common Mobile Vision API Support for fast image and video on-device detection and tracking. NEW!
- 16. 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
- 17. Face API Photo credit developers.google.com/vision
- 18. Easy to use Java API image detected items Detector 1. Create a detector object 2. detectedItems = detector.detect(image) Photo credit developers.google.com/vision
- 19. Text Detection Latin based language Understand text structure Photo credit Getty Images
- 21. Barcode Detection 1D barcodes EAN-13/8 UPC-A/E Code-39/93/128 ITF Codabar 2D barcodes QR Code Data Matrix PDF-417 AZTEC UPC DataMatrix QR Code PDF 417 Video and image credit Google
- 22. Combined Vision & Translation
- 23. Mobile Vision: Codelabs and Samples Googly Eyes Code Sample github.com/googlesamples/android-vision/tree/master/visionSamples/googly-eyes Codelabs codelabs.developers.google.com/codelabs/face-detection/ codelabs.developers.google.com/codelabs/mobile-vision-ocr/ Mobile Vision Developers developers.google.com/vision/ GitHub Code Samples github.com/googlesamples/android-vision Stack Overflow Find and ask questions under the android-vision tag.
- 24. ● Open source Machine Learning library ● Especially useful for Deep Learning ● For research and production ● Apache 2.0 license
- 25. Hello World Image from https://github.com/mnielsen/neural-networks-and-deep-learning ?
- 26. What we see What the computer “sees”
- 29. Complete code import tensorflow as tf mnist = tf.contrib.learn.datasets.load_dataset('mnist') classifier = tf.learn.LinearClassifier(n_classes=10) classifier.fit(mnist.train.images, mnist.train.labels) score = metrics.accuracy_score(mnist.test.labels, classifier.predict(mnist.test.images)) print('Accuracy: {0:f}'.format(score))
- 31. Lots of tutorials at tensorflow.org
- 32. Codelab - goo.gl/xGsB9d Video - goo.gl/B2zYWN TensorFlow for Poets
- 34. Claude Monet - Bouquet of Sunflowers Images from the Metropolitan Museum of Art (with permission) Image by @random_forests
- 35. A little more TensorFlow
- 36. A multidimensional array. A graph of operations.
- 37. Data Flow Graphs Computation is defined as a directed acyclic graph (DAG) to optimize an objective function ● Graph is defined in high-level language (Python) ● Graph is compiled and optimized ● Graph is executed (in parts or fully) on available low level devices (CPU, GPU) ● Data (tensors) flow through the graph ● TensorFlow can compute gradients automatically
- 38. Architecture ● Core 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 ...
- 39. Raspberry Pi DatacentersYour laptop Android iOS Portable & Scalable
- 40. /Serving Open-source solution for serving trained models
- 41. Examples of Machine Learning
- 43. spotted salamander fire salamander retriever Recognizing images with Inception https://research.googleblog.com/2016/03/train-your-own-image-classifier-with.html
- 46. Image source: Wikimedia + = A Neural Algorithm of Artistic Style http://arxiv.org/abs/1508.06576 ?
- 47. Image source: Wikimedia + = A Neural Algorithm of Artistic Style http://arxiv.org/abs/1508.06576
- 48. Image source: Wikimedia + = A Neural Algorithm of Artistic Style http://arxiv.org/abs/1508.06576 goo.gl/fyDxhC
- 53. Smart reply in Inbox by Gmail 10% of all responses sent on mobile
- 54. tensorflow.org github.com/tensorflow Want to learn more? Udacity class on Deep Learning, goo.gl/iHssII 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 What's Next