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Introduction to Deep Learning on Azure - Global Azure Bootcamp 2018

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Slides from my presentation on Introduction to Deep Learning on Azure presented at Global Azure Bootcamp 2018.

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Introduction to Deep Learning on Azure - Global Azure Bootcamp 2018

  1. 1. INTRODUCTION TO DEEP LEARNING ON AZURE SHASHI JEEVAN M P @shashijeevan https://shashijeevan.com
  2. 2. DEFINITION • Artificial Intelligence • Machine Learning • Deep Learning
  3. 3. MACHINE LEARNING DEFINITION • Machine Learning: Field of study that gives computers the ability to learn without being explicitly programmed. ~Arthur Samuel, 1959 • Well posed Learning Problem: A computer program is said to learn from experience E with respect to some task T and some performance measure P, if its performance on T, as measured by P, improves with experience E. ~Tom Mitchell, 1998
  4. 4. MACHINE LEARNING TYPES • Supervised Learning – Naïve Bayes, SVM, Artificial Neural Nets, Random Forest • Unsupervised Learning – K Means Clustering • Reinforcement Learning – Model Free Learning, MDP, Q Learning • Semi Supervised Learning – GAN (New)
  5. 5. PREDICTION • Regression • Classification
  6. 6. HOW TO PREDICT HOUSE PRICE? • Identify the features and price • Train using the samples with known price • Use the new house data and predict price
  7. 7. LINEAR REGRESSION • Best fit line Y = m * X + B Minimize Errors using Least Squares method
  8. 8. APPLICATIONS • Netflix • Uber https://eng.uber.com/michelangelo/ • Amazon shopping
  9. 9. Machine Learning Reference Architecture
  10. 10. NEURAL NETWORKS • Huge number of features (Hundreds) • Identify the features automatically
  11. 11. DEEP NEURAL NETWORKS • Specialized Neural Networks • Lots of Hidden layers (Typically Hundreds) • Uses latest Techniques • ReLU activation • Residual Networks • Specific architectures to solve specific problems
  12. 12. NETWORK ARCHITECTURES • https://becominghuman.ai/cheat-sheets-for-ai-neural- networks-machine-learning-deep-learning-big-data- 678c51b4b463
  13. 13. MICROSOFT ARCHITECTURE • Residual Networks • 152 Layers • De-facto Standard for image classification with CNN
  14. 14. JARGON • Loss Function • Gradient Descent • Back propagation
  15. 15. TYPES OF APPLICATIONS • Classification • Regression
  16. 16. POPULAR USES • Self driving cars • Text Translations • Automatic colorization of B&W images • Sentiment Analysis
  17. 17. PIPELINE • Build Model • Train • Inference for Prediction
  18. 18. BUILDING MODEL • Layers are organized • Activation function –ReLU, Sigmoid, Softmax • Loss function • Optimizer
  19. 19. TRAINING • Training Set/Testing Set • Batch • Weights adjustment • Epoch • Loss • Accuracy
  20. 20. INFERENCE • Saved model is used • Untrained Input is processed and a prediction is made
  21. 21. AZURE • Data Science VM • Deep Learning VM • GPU support (NC- Series) • Use Azure Trial (need to convert to Paid account) • Trial account has 4 cores • Deep Learning VM requires minimum 6 cores • Specific Regions • East US, East US 2, North Central US, South Central US and West US 2
  22. 22. AZURE SUPPORTED • Nvidia GPU • GPU version of Deep Learning Frameworks • Jupyter Hub • X2Go
  23. 23. SETUP ON AZURE • Create Deep Learning VM Ubuntu • Setup Auto shutdown • Connect using SSH with the hostname (IP Address is reset on restart) • Build Model • Train • Infer
  24. 24. TYPICAL USES • Ready to use for development • Consistent setup for a team • Use it for a temporary training tasks
  25. 25. DEMO • Training • MNIST dataset • Keras with Microsoft CNTK backend • Inference • Use trained model • Predict the digit in Jupyter • Write and Predict
  26. 26. DEMO CODE LINKS • Jupyter Notebooks for Training and Prediction https://github.com/shashijeevan/keras_mnist_notebooks • Python Flask App for generating digits and predict https://github.com/shashijeevan/mnist-draw
  27. 27. NEXT STEPS • ONNX – Model Exchange Standard • Windows ML – Inference Built into Windows • Visual Studio Tools for AI
  28. 28. RESOURCES • https://academy.microsoft.com/en-us/professional- program/tracks/artificial-intelligence/ • https://www.analyticsvidhya.com • https://www.coursera.org/ • https://machinelearningmastery.com/handwritten-digit- recognition-using-convolutional-neural-networks-python- keras/

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