1. Deep learning for Dummies
- Ashok
Govindarajan
20-12-2017 Technology sharing series 1
2. Contents
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• Introduction
Artificial Intelligence
Machine Learning
Deep Learning
• Examples – what is an input and what is an output in a deep learning system
• Use-cases – where are the potential applications?
• Common Libraries used in the Python context – keras.io
• Conclusion
3. What is deep learning?
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Technology sharing series 3
Takeaway : Timelines and definitions
4. Definition of Deep learning
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Technology sharing series 4
Deep learning is a branch of machine learning often applied to image recognition
that uses algorithms to learn in multiple levels corresponding to different levels of
abstraction.
It typically relies on complex neural networks.
5. Training and inference – stages of deep
learning
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Technology sharing series 5
Takeaway : Training with known data (training dataset) and using the
updated model for inference(with new data).
6. Layer-wise split of deep learning
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Takeaway : Layer-level split
7. Examples
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What is an input and output in a deep-learning system?
Refer Page 440 in the following link :
http://graveleylab.cam.uchc.edu/WebData/mduff/MEDS_6498_SPRING_2016/d
eep_learning_nature_2015.pdf
8. What is the use-case?
20-12-2017
Technology sharing series 8
• As part of the smart-phone and cloud eco-system, how is deep learning going to
integrate and what are the challenges?
• https://www.technologyreview.com/s/534736/deep-learning-squeezed-onto-a-
phone/
• http://www.dqindia.com/intelligent-devices-device-prediction-learning/
.
9. Common Libraries used in Python context
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Technology sharing series 9
• Keras – higher level library in Python that uses Tensorflow
https://keras.io/
Keras provides an additional layer of NN primitives with Theano and TensorFlow
as its back-end. It has a highly customizable interface to quickly experiment with
and deploy deep NNs, and has become our primary tool used to generate
numerical results.
Other libraries of interest include :
Caffe , MxNet, Tensor Flow, Theoano, Torch
Some aspects of keras cheat sheet will be discussed
10. Conclusion
20-12-2017 Technology sharing series 10
• The future of deep-learning
Unsupervised learning had a catalytic effect in reviving interest in deep learning, but
has since been overshadowed by the successes of purely supervised learning. Although
we have not focused on it in this review, we expect unsupervised learning to become
far more important in the longer term.
Human and animal learning is largely unsupervised: we discover the structure of the
world by observing it, not by being told the name of every object
11. Acknowledgements and References
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Technology sharing series 11
• http://graveleylab.cam.uchc.edu/WebData/mduff/MEDS_6498_SPRING_2016
/deep_learning_nature_2015.pdf
• http://fortune.com/ai-artificial-intelligence-deep-machine-learning/
• https://www.technologyreview.com/s/534736/deep-learning-squeezed-onto-
a-phone/
• https://www.computerworld.com/article/3027217/smartphones/
deep-learning-is-coming-to-your-android-phone.html