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Leveraging Deep Learning algorithms to business problems - breaking myths

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Applications of Machine Learning and Deep Learning algorithms help businesses solve day-to-day problems, understand customers, and gain analytical insights / solutions for better Business Intelligence. Deep Learning algorithms utilizes intelligent automation to help businesses grow and stay ahead of competition.

In this webinar, we’ve untangled Deep Learning myths, and talked about some techniques, how to apply them and also identified problems that benefit from Deep Learning Algorithms

Click here and listen to our webinar on Deep Learning Algorithms : https://www.imaginea.com/deep-learning-webinar/

Or connect to us at: connect@imaginea.com

Published in: Technology

Leveraging Deep Learning algorithms to business problems - breaking myths

  1. 1. BUSTING DEEP LEARNING MYTHS 1Private and confidential. Copyright (C) 2017, Imaginea Technologies Inc. All rights reserve. WEBINAR PRESENTATION
  2. 2. Suresh Babu Chief Revenue Officer Speakers Srikumar Subramanian Director – Technology 2Private and confidential. Copyright (C) 2017, Imaginea Technologies Inc. All rights reserve.
  3. 3. Deep Learning: Overview Understand and generate natural language Classify and label images Retrieve information from unstructured data Analyze and detect fraud in real time Be creative with image analytics Sense your surroundings and trigger actions  Deep learning has become the go-to technique for tackling problems for which we can state test cases but are unable to come up with algorithms  Deep learning is fast emerging as a general purpose tool to solve a broad class of problems 3Private and confidential. Copyright (C) 2017, Imaginea Technologies Inc. All rights reserve.
  4. 4. KEY TAKEAWAYS Find out how you can leverage deep learning for business Find ways to leverage pre-trained networks Know how knowledge of conventional machine learning is indispensable Have options for getting training data     4Private and confidential. Copyright (C) 2017, Imaginea Technologies Inc. All rights reserve.
  5. 5. DEEP LEARNING JOURNEY SO FAR 1980 Training them becomes difficult due to inadequate compute & dataThe power of neural networks is recognized GPUs make the required compute easily accessible for a broad range of problems Vast amounts of data flow through the internet Techniques for training "deep" networks open up new opportunities "Convolutional" networks – DCNNs, in particular – have changed the image analysis landscape 1990 2012 2015 2017 5Private and confidential. Copyright (C) 2017, Imaginea Technologies Inc. All rights reserve.
  6. 6. 30K strong community Social experience of finding & sharing interesting typographic content Deep learning application  Identifies images without typographic content and marks them as spam  Transfers the image style to a text using Deep Convolutional Neural Network (DCNN) technique Fontli: social network for typography enthusiasts 6Private and confidential. Copyright (C) 2017, Imaginea Technologies Inc. All rights reserve.
  7. 7. The algorithm identifies images without typographic content and marks them as spam Uses DCNN technique We used 45,000 images to train the network Achieves 90% accuracy based on historical data IMAGE SPAM No typographic text in the image, hence marked as spam IMAGE ACCEPTED Typographic text in the image, hence accepted Fontli: typographic image spam filtering 7Private and confidential. Copyright (C) 2017, Imaginea Technologies Inc. All rights reserve.
  8. 8. THE WHEAT THE CHAFF 8Private and confidential. Copyright (C) 2017, Imaginea Technologies Inc. All rights reserve.
  9. 9. Input Output Trust Transfers the image style to a text using Deep Convolutional Neural Network (DCNN) technique The algorithm extracts style (texture/ high level pattern) from style image and renders the given content image Uses 19-layer network which is the default in the neural- style tool Style transfer for typography 9Private and confidential. Copyright (C) 2017, Imaginea Technologies Inc. All rights reserve.
  10. 10. DEEP NEURAL NETWORK SHALLOW LAYERS Sensory features DEEP LAYERS Higher cognitive features Deep Neural Networks 10Private and confidential. Copyright (C) 2017, Imaginea Technologies Inc. All rights reserve.
  11. 11. Understanding Deep Convolutional Neural Network (DCNN) Input list of numbers Deep Neural Network Output list of numbers 11Private and confidential. Copyright (C) 2017, Imaginea Technologies Inc. All rights reserve.
  12. 12. #DeepLearningMyth There are tons of myths around deep learning The most popular being “It’s for complex problems only” Let’s cut through these myths and find out what lies at the heart of it all 12Private and confidential. Copyright (C) 2017, Imaginea Technologies Inc. All rights reserve.
  13. 13. 13Private and confidential. Copyright (C) 2017, Imaginea Technologies Inc. All rights reserve.
  14. 14. FONTLI: 45,000 images used to train the network 14Private and confidential. Copyright (C) 2017, Imaginea Technologies Inc. All rights reserve.
  15. 15. Which whale is it, anyway? Face recognition to identify whales using deep learning A non-random sample from the dataset 4544 training images only & 447 categories 15Private and confidential. Copyright (C) 2017, Imaginea Technologies Inc. All rights reserve. Source: NOAA Fisheries
  16. 16. Transfer learning: the open secret Don’t need a billion of anything in order to use DCNNs Can take an already trained network, bring in your domain- specific labeled data and profit Need to understand applicability of domain. For vision tasks, a network pre- trained on, say, the ImageNet collection is a great starting point 16Private and confidential. Copyright (C) 2017, Imaginea Technologies Inc. All rights reserve.
  17. 17. Transfer learning architecture 17Private and confidential. Copyright (C) 2017, Imaginea Technologies Inc. All rights reserve. Input image (256x256) WEIGHTS BORROWED FROM VGG19 WEIGHTS TRAINED WITH NEW DATA
  18. 18. 18Private and confidential. Copyright (C) 2017, Imaginea Technologies Inc. All rights reserve.
  19. 19. You do need powerful hardware to keep your training cycles and hyper- parameter searches within reasonable time scales. You don’t need the same powerful hardware to use the networks. Normal laptops and phones today have enough compute capacity to perform predictions using pre-trained networks. Use Transfer Learning and incrementally teach a network only about your data. Training and deployment have different needs TRAIN DEPLOY TRANSFER LEARNING 19Private and confidential. Copyright (C) 2017, Imaginea Technologies Inc. All rights reserve.
  20. 20. 20Private and confidential. Copyright (C) 2017, Imaginea Technologies Inc. All rights reserve.
  21. 21.  Autoencoders produce “word embeddings” unsupervised  E.g., Queen = king - man + woman  Take a network trained for, say, image classification  Strip out the last few fully connected layers  Reuse the other parts as feature detectors  Standard question: Is this an image of a cat?  Twisted question: How do I tweak the input image to make it more cat-like? Many applications for different architectures Understanding words Repurposing networks Domain synthesis 21Private and confidential. Copyright (C) 2017, Imaginea Technologies Inc. All rights reserve.
  22. 22. Autocoding fields extracted from documents 22Private and confidential. Copyright (C) 2017, Imaginea Technologies Inc. All rights reserve.
  23. 23. The concept space of words Source: www.samyzaf.com/ML/nlp 23Private and confidential. Copyright (C) 2017, Imaginea Technologies Inc. All rights reserve.
  24. 24. 24Private and confidential. Copyright (C) 2017, Imaginea Technologies Inc. All rights reserve.
  25. 25.  Deep learning doesn’t absolve you from data preparation responsibilities  Data sufficiency  Data quality  Feature size and significance  Including ambiguous cases  Synthetic data  Training/ Validation/ Test sets, batched training, epochs  Recognizing and dealing with overfitting  Basic probability and statistics  Discipline to ensure interpretation validity Machine Learning expertise is required Data preparation Know best practices for supervised learning 25Private and confidential. Copyright (C) 2017, Imaginea Technologies Inc. All rights reserve.
  26. 26. 26Private and confidential. Copyright (C) 2017, Imaginea Technologies Inc. All rights reserve.
  27. 27. Hmmm… not really Some features can be automatically learnt and reused Representations need careful design and study Combine representations produced by different trained neural networks E.g., unsupervised learning of word embedding E.g., skip-gram and CBOW representations for text E.g., Region proposal networks for object detection “Features” stand for derived attributes of the data which are relevant to the problem E.g., Detecting eyes + mouth may be adequate to detect and position a face in a scene 27Private and confidential. Copyright (C) 2017, Imaginea Technologies Inc. All rights reserve.
  28. 28. 28Private and confidential. Copyright (C) 2017, Imaginea Technologies Inc. All rights reserve.
  29. 29. You may be able to generate your data! Many real world problems already have good models for going one way … but we want to go the other way E.g., Computer graphics algorithms for photo-realistic scene rendering E.g., From a photograph to scene understanding So generate data using these known models and train your networks! OPEN AI UNIVERSE 29Private and confidential. Copyright (C) 2017, Imaginea Technologies Inc. All rights reserve.
  30. 30. Training deep learning networks for self driving cars using Grand Theft Auto! 30Private and confidential. Copyright (C) 2017, Imaginea Technologies Inc. All rights reserve.
  31. 31. RECAP Focus on quality and completeness of your domain’s data. Everything depends on it Use “Transfer Learning” to leverage other pre-trained networks to tackle your problem Knowledge of conventional machine learning is important for your staff even if you use deep learning Synthesize your data if your problem domain permits it 31Private and confidential. Copyright (C) 2017, Imaginea Technologies Inc. All rights reserve.
  32. 32. Pramati’s M&As of leading products Innovation enablement Serving over 200 product companies Agile methodology User-centric design Serving from 5 global locations Products built from conception-code-cash Unique products & services 1300+ engineers About Imaginea 32Private and confidential. Copyright (C) 2017, Imaginea Technologies Inc. All rights reserve.
  33. 33. Deep Learning: Our services 33Private and confidential. Copyright (C) 2017, Imaginea Technologies Inc. All rights reserve.
  34. 34. 35Private and confidential. Copyright (C) 2017, Imaginea Technologies Inc. All rights reserve.
  35. 35. Get in touch … Suresh Babu Chief Revenue Officer suresh.babu@imaginea.com Srikumar Subramanian Director – Technology sriku@imaginea.com 36Private and confidential. Copyright (C) 2017, Imaginea Technologies Inc. All rights reserve.
  36. 36. Disclaimer This document may contain forward-looking statements concerning products and strategies. These statements are based on management's current expectations and actual results may differ materially from those projected, as a result of certain risks, uncertainties and assumptions, including but not limited to: the growth of the markets addressed by our products and our customers' products, the demand for and market acceptance of our products; our ability to successfully compete in the markets in which we do business; our ability to successfully address the cost structure of our offerings; the ability to develop and implement new technologies and to obtain protection for the related intellectual property; and our ability to realize financial and strategic benefits of past and future transactions. These forward-looking statements are made only as of the date indicated, and the company disclaims any obligation to update or revise the information contained in any forward-looking statements, whether as a result of new information, future events or otherwise. All Trademarks and other registered marks belong to their respective owners. Copyright © 2017, Imaginea Technologies, Inc. and/or its affiliates. All rights reserved. Credits Images under Creative Commons Zero license. 37Private and confidential. Copyright (C) 2017, Imaginea Technologies Inc. All rights reserve.

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