This document discusses applications of deep learning, including computer vision, speech recognition, and natural language processing. It notes that deep learning is driven by scale - large neural networks with huge amounts of labeled training data can perform complex tasks like image captioning and question answering. Deep learning techniques are being applied across many domains from mobile devices to data centers and are powering intelligent assistants with capabilities like vision and speech recognition.

1. Andrew Ng
What data scientists should know
about deep learning

2. Andrew NgAndrew Ng
Images Speech Behavior
Applications of Deep Learning

3. Andrew NgAndrew Ng
Computer vision:
Find coffee mug

4. Andrew NgAndrew Ng
Computer vision:
Find coffee mug
Early, poor computer vision results.

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Neurons in the brain
Output
Deep Learning: Neural network

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Computer vision

7. Andrew NgAndrew Ng
Yes/No
(Mug or not?)
What is a neural network?
Data (image)
x1
λ 5
, x2
λ 5
x2
=(W1
´x1
)+
x3
=(W2
´x2
)+
x1 x2 x3
x4
x5
W4W3W2W1

8. Andrew NgAndrew Ng
Supervised learning (learning from labeled data)
Yes
No
YX
Image Yes/No
(Is it a coffee mug?)
Data:

9. Andrew NgAndrew Ng
Engine
Fuel
Large neural networks
Labeled data
(x,y pairs)
Why is Deep Learning taking off?

10. Andrew NgAndrew Ng
It’s all about scale.
almost

11. Andrew NgAndrew Ng
Rocket engines: Deep Learning driven by scale
1 million
connections
(2007)
CPU
10 million
connections
(2008)
GPU
1 billion
connections
(2011)
Cloud
(many CPUs)
100 billion
connections
(2015)
HPC
(many GPUs)

12. Andrew NgAndrew Ng
A yellow bus driving down a road
with green trees and green grass
in the background.
Living room with white couch and
blue carpeting. The room in the
apartment gets some afternoon sun.
Can a computer understand these pictures?

13. Andrew NgAndrew Ng
Supervised learning (learning from labeled data)
YX
Image Caption
A yellow bus driving down a
road with green trees and
green grass in the background.

14. Andrew NgAndrew Ng
CaptionData (image)
A yellow bus
driving down….
Learning to Caption

15. Andrew NgAndrew Ng
( , )
Learning to answer questions
YX
(Image,Question) Answer
The bus is red.
(公共汽车是红色的)
What is the color
of the bus?
(公共汽车是设么颜色的？)

16. Andrew NgAndrew Ng
What is the
color of the
bus?
Data
(image, question)
The bus is red ....
Learning to Answer Questions

17. Andrew Ng

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19. Andrew NgAndrew Ng
Images Speech Behavior
Applications of Deep Learning

20. Andrew NgAndrew Ng
Transcript
Audio
Features
Data (audio)
Phonemes
Language
model
Speech recognition

21. Andrew NgAndrew Ng
TranscriptData (audio)
Baidu Deep Speech: The rocket engine

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0
20000
40000
60000
80000
100000
120000
WSJ Switchboard Fisher Deep Speech
80 300 2000
>100,000
Synthesized
data
Hours of data
Dataset
Baidu Deep Speech: The rocket fuel (data)

23. Andrew NgAndrew Ng
Speech recognition performance
Error

24. Andrew NgAndrew Ng
With 99% accuracy, we could
redesign your cellphone using a
speech interface.
Most people don’t understand the difference
between 95% accuracy and 99% accuracy.
99% is game changing.

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Home appliances
(e.g., TV, microwave,
music player, ….)
Car interfaces Wearables
Speech will transform the Internet of Things

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Images Speech Behavior
Applications of Deep Learning

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Web search/Advertising Datacenter management Computer security
Deep Learning and big data

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Applications of Deep Learning
Images Speech Behavior

29. Andrew NgAndrew Ng
It’s all about scale.
almost

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Why deep learning
Amount of data
Performance Older learning
algorithms
Deep learning
How do data science techniques scale with amount of data?

31. Andrew NgAndrew Ng
The problem of scale: Mobile devices
Image 72 keypoints Face segmentation
Binary size Speed
Desktop model 153MB 1.25 fps
Mobile model 800 KB
(190x reduction)
25 fps
(20x speedup)

32. Andrew NgAndrew Ng
Faceyou
(available on Apple
app store)

33. Andrew NgAndrew Ng
The future of Deep Learning
The AI rocket
Images Speech Behavior

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We have superpowers
AI Data Science