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Deep Learning Workshop

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Deep Learning is an implementation of Machine Learning that uses neural networks to solve difficult problems such as image recognition, sentiment analysis and recommendations. Neural networks simulate the functions of the brain where artificial neurons work in concert to detect patterns in data. This allows deep learning algorithms to classify, predict and recommend with an increasing degree of accuracy as more data is trained in the network.

This workshop will walk you through the basics of Deep Learning, introduce you to a very powerful open-source Deep Learning framework called Apache MXNet and guide you in training a neural network using Apache MXNet on AWS.

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Deep Learning Workshop

  1. 1. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Osemeke Isibor, Solutions Architect. iosemeke@amazon.com 22nd November 2017 What is Deep Learning, uses and benefits
  2. 2. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Objectives • Help you understand what Deep Learning is. • Encourage you to start thinking where you can apply Deep Learning in your organization or business. • Master how to create deep learning algorithms and training models • Motivate you to acquire more knowledge and get started with Deep Learning.
  3. 3. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Agenda • Introduction • The basics of Deep Learning • Deep Learning case studies • Conclusion
  4. 4. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Introduction Artificial Intelligence Machine Learning Deep Learning
  5. 5. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Types of Machine Learning Supervised learning Human intervention and validation required e.g. Photo classification and tagging Unsupervised learning No human intervention required e.g. Auto-classification of documents based on context
  6. 6. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Supervised Machine Learning Process • Input feature selection – • what are my predictions going to be based on. • Target – • what you want to predict. • Prediction function – • regression, classification, dimensionality reduction e.t.c
  7. 7. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Model Performance Measurement All Labeled Dataset Training Data 70% 30% Training Test Data Evaluation Result Trial Model Accuracy
  8. 8. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
  9. 9. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Machine Learning – When to Use It You need ML if: •Simple classification rules are inadequate •Scalability is an issue with large number of datasets You do not need ML if: •You can predict the answers by using simple rules and computations •You can program predetermined steps without needing any data driven learning
  10. 10. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Going Beyond Machine Learning
  11. 11. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Deep Learning – Advanced Machine Learning
  12. 12. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. What is Deep Learning? Deep learning is a class of machine learning algorithms that: •Use a cascade of many layers of nonlinear processing units for feature extraction and transformation. •Each successive layer uses the output from the previous layer as input. The algorithms may be supervised or unsupervised and applications include pattern analysis (unsupervised) and classification (supervised). •Learn multiple levels of representations that correspond to different levels of abstraction; the levels form a hierarchy of concepts.
  13. 13. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Performance of Deep Learning Vs Machine Learning Data Performance Traditional Machine Learning Algorithms Deep Learning Algorithms
  14. 14. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. The Secret Sauce – Neural Networks A collection of simple, trainable mathematical units that collectively learn complex functions ü Output Neural network Input Hidden layers
  15. 15. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Human Brain Neuron Inputs Output
  16. 16. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Deep Learning Training – Image Classification X Seal ü Grizzly Bear ü Polar Bear ü Dog ü Fox Feedback / Back Propagation Neural network
  17. 17. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Deep Learning – Data Representation Hierarchy of representations • Image – vectors of pixel, motif, part, contour, edge, etc. • Videos – Image frames, pixels per frame, deltas per frame, etc. • Text – characters, words, clauses, sentences, etc. • Speech – audio, band, frequency, wavelengths, modulations, phonetics, etc.
  18. 18. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Deep Learning – Advantages • Features automatically deduced and optimally tuned for the desired outcome • Robustness to variations automatically learned • Reusability – same neural network approach can be used for many applications and data types • Massively parallel computations through use of GPUs – scalable for large volumes of data
  19. 19. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Some application of Deep Learning using AWS
  20. 20. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
  21. 21. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
  22. 22. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
  23. 23. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
  24. 24. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
  25. 25. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
  26. 26. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
  27. 27. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
  28. 28. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. And Many more use cases ASR/NLU Language Translation Self Driving Cars Playing Go Financial Risk Medical Diagnosis
  29. 29. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Conclusion: Deep Learning is very very Powerful • Tons of applications and use cases but NOT for ALL cases. • Many algorithms, frameworks and tools. • Easy and cost effective to start and experiment on AWS using latest generation GPU instances.
  30. 30. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Get Started https://aws.amazon.com/deep-learning/
  31. 31. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Thank You Osemeke Isibor, Solutions Architect iosemeke@amazon.com

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