1. Introduction to Deep Learning

2. Google Brain
There Google scientists created one of the largest deep neural networks by connecting 16,000 computer processors, They wanted to test if computer can learn itself or not.
They presented Google brain with 10 million digital images found in YouTube videos, what did Google’s brain do after viewing these images for three days?

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4. Project Adam
Microsoft built a highly efficient, highly scalable distributed system from commodity PCs to train deep neural networks
They use 14 million images from ImageNet to train the deep neural network

5. Project Adam can identify dogs
Project Adam can identify kinds of dogs - Corgi
Project Adam can identify particular breeds of Corgi: Pembrake vs Cardigan Corgi

6. Demo Systems

7. Highway Perception
Copied from “An Empirical Evaluation of Deep Learning on Highway Driving”

8. Microsoft Speech Translation
Chief Research Officer of Microsoft Research Rick Rashid demonstrates a speech recognition breakthrough patterned after deep neural networks

9. Deep Learning in IQ Test
Isotherm is to temperature as isobar is to? (i) atmosphere, (ii) wind, (iii) pressure, (iv) latitude, (v) current.
Which is the odd one out? (i) calm, (ii) quiet, (iii) relaxed, (iv) serene, (v) unruffled.
Which word is most opposite to MUSICAL? (i) discordant, (ii) loud, (iii) lyrical, (iv) verbal, (v) euphonious.
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11. Medical Diagnosis
Deep learning technology has been applied to medical diagnosis based on a large amount of accumulated X-rays, CT scans, lab data and MRIs
Diagnosis by deep learning are typically more objective and accurate according to some studies.

12. Image credit: Shutterstock
Deep Genomics
 A new startup led by University of Toronto professor Brendan Frey use deep learning to identify gene variants and mutations never before observed or studied and find how these link to various diseases
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Image credit: Shutterstock

13. A Little History of Neural Network
1958: Frank Rosenblatt (1958) created the perceptron.
1969: Marvin Minsky and Seymour Papert discovered that perceptron failed to solve exclusive-or problem. 
1975: backpropagation learning algorithm was proposed by Werbos

14. 1992: Kernel Machines were proposed for nonlinear classification
Between 2009 and 2012: Deep Learning methods achieved great success
- Deep learning was shown achieving human-competitive or even superhuman performance on important benchmarks
- A team from Georff Hinton’s lab at University of Toronto won a 2012 contest sponsored by Merck to design software to help find molecules that might lead to new drugs.[29]

15. Deep Learning Vs Traditional Machine Learning
Deep learning: don’t need to provide features ahead of time, it learns features at different level by itself.
The same deep learning architecture can be trained to accomplish different tasks.
Deep learning is the solution to big data

17. Deep Learning Vs IBM Watson

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