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How can you get started with machine learning?

How can you get started with machine learning?

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.

Abdelrahman Omran

March 09, 2017

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  1. 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
  2. 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
  3. 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 API 1 2 3
  4. Confidential & Proprietary Google 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
  5. Confidential & Proprietary Google 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
  6. 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