This document provides an introduction to deep learning, including definitions of artificial intelligence, machine learning, and deep learning. It discusses examples of inputs and outputs in deep learning systems, potential applications, common Python libraries like Keras, and conclusions. The key takeaways are that deep learning uses neural networks to learn patterns at different levels of abstraction, it involves training models on data and using the models to make inferences on new data, and libraries like Keras and TensorFlow are commonly used.

1. Deep learning for Dummies
- Ashok
Govindarajan
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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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Takeaway : Timelines and definitions

4. Definition of Deep learning
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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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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?
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• 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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• 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
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• 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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• 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

12. 20-12-2017 Technology sharing series 12
Thank You