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Page 1: Deep Learning Part 2 Other networks · Slides by: Joseph E. Gonzalez, jegonzal@berkeley.edu Deep Learning Part 2 Other networks

Slides by:

Joseph E. Gonzalez,

[email protected]

Deep Learning Part 2Other networks

mailto:[email protected]
Page 2: Deep Learning Part 2 Other networks · Slides by: Joseph E. Gonzalez, jegonzal@berkeley.edu Deep Learning Part 2 Other networks

Quick Logistics

Ø Please sign up to present

Ø Complete reading questions before lecture

Ø Skim the reading for the lecture you are presenting (later in semester).Ø Is there a better paper?

http://bit.ly/aisys-sp19

http://bit.ly/aisys-sp19
Page 3: Deep Learning Part 2 Other networks · Slides by: Joseph E. Gonzalez, jegonzal@berkeley.edu Deep Learning Part 2 Other networks

Machine Learning ≈ Function Approximation

Label:Cat

Object Recognition Speech Recognition

“The cat in the hat”

Robotic Control

The cat in the hat.

El gato en el sombrero

Machine Translation

Last Time

Page 4: Deep Learning Part 2 Other networks · Slides by: Joseph E. Gonzalez, jegonzal@berkeley.edu Deep Learning Part 2 Other networks

Architectures for Different kinds of inputs

Convolutional Networks

The quick brown fox…

The cat in the hat.

El gato en el sombrero

Recurrent Networks

Sequential reasoning tasksspatial reasoning tasks

Speech recognition

ReinforcementLearning

Page 5: Deep Learning Part 2 Other networks · Slides by: Joseph E. Gonzalez, jegonzal@berkeley.edu Deep Learning Part 2 Other networks

Architectures for Different kinds of inputs

Convolutional Networks

The quick brown fox…

The cat in the hat.

El gato en el sombrero

Recurrent Networks

Sequential reasoning taskspatial reasoning tasks

Speech recognition

ReinforcementLearning

Graph Networks

Operating on graph data

Link Prediction

Graph Embedding

Page 6: Deep Learning Part 2 Other networks · Slides by: Joseph E. Gonzalez, jegonzal@berkeley.edu Deep Learning Part 2 Other networks

Supervised Machine LearningØ Given data containing the function inputs and outputs

Input Output

X1 Y1

X2 Y2

… …

Xn Yn

cat

baby

Data

baby

f✓(x) ! y<latexit sha1_base64="cuKQEy7Pm3MosVAVLe4DhhYax+Y=">AAACAnicbVDLSsNAFJ3UV62vqCtxM1iEuimJCLosunFZwT6gCWEynTRDJw9mbtQSiht/xY0LRdz6Fe78G6dtFtp64MLhnHu59x4/FVyBZX0bpaXlldW18nplY3Nre8fc3WurJJOUtWgiEtn1iWKCx6wFHATrppKRyBes4w+vJn7njknFk/gWRilzIzKIecApAS155kHgORAyILWHE+xIPgiBSJnc45FnVq26NQVeJHZBqqhA0zO/nH5Cs4jFQAVRqmdbKbg5kcCpYOOKkymWEjokA9bTNCYRU24+fWGMj7XSx0EidcWAp+rviZxESo0iX3dGBEI1703E/7xeBsGFm/M4zYDFdLYoyASGBE/ywH0uGQUx0oRQyfWtmIZEEgo6tYoOwZ5/eZG0T+u2VbdvzqqNyyKOMjpER6iGbHSOGugaNVELUfSIntErejOejBfj3fiYtZaMYmYf/YHx+QPDc5cD</latexit><latexit sha1_base64="cuKQEy7Pm3MosVAVLe4DhhYax+Y=">AAACAnicbVDLSsNAFJ3UV62vqCtxM1iEuimJCLosunFZwT6gCWEynTRDJw9mbtQSiht/xY0LRdz6Fe78G6dtFtp64MLhnHu59x4/FVyBZX0bpaXlldW18nplY3Nre8fc3WurJJOUtWgiEtn1iWKCx6wFHATrppKRyBes4w+vJn7njknFk/gWRilzIzKIecApAS155kHgORAyILWHE+xIPgiBSJnc45FnVq26NQVeJHZBqqhA0zO/nH5Cs4jFQAVRqmdbKbg5kcCpYOOKkymWEjokA9bTNCYRU24+fWGMj7XSx0EidcWAp+rviZxESo0iX3dGBEI1703E/7xeBsGFm/M4zYDFdLYoyASGBE/ywH0uGQUx0oRQyfWtmIZEEgo6tYoOwZ5/eZG0T+u2VbdvzqqNyyKOMjpER6iGbHSOGugaNVELUfSIntErejOejBfj3fiYtZaMYmYf/YHx+QPDc5cD</latexit><latexit sha1_base64="cuKQEy7Pm3MosVAVLe4DhhYax+Y=">AAACAnicbVDLSsNAFJ3UV62vqCtxM1iEuimJCLosunFZwT6gCWEynTRDJw9mbtQSiht/xY0LRdz6Fe78G6dtFtp64MLhnHu59x4/FVyBZX0bpaXlldW18nplY3Nre8fc3WurJJOUtWgiEtn1iWKCx6wFHATrppKRyBes4w+vJn7njknFk/gWRilzIzKIecApAS155kHgORAyILWHE+xIPgiBSJnc45FnVq26NQVeJHZBqqhA0zO/nH5Cs4jFQAVRqmdbKbg5kcCpYOOKkymWEjokA9bTNCYRU24+fWGMj7XSx0EidcWAp+rviZxESo0iX3dGBEI1703E/7xeBsGFm/M4zYDFdLYoyASGBE/ywH0uGQUx0oRQyfWtmIZEEgo6tYoOwZ5/eZG0T+u2VbdvzqqNyyKOMjpER6iGbHSOGugaNVELUfSIntErejOejBfj3fiYtZaMYmYf/YHx+QPDc5cD</latexit><latexit sha1_base64="cuKQEy7Pm3MosVAVLe4DhhYax+Y=">AAACAnicbVDLSsNAFJ3UV62vqCtxM1iEuimJCLosunFZwT6gCWEynTRDJw9mbtQSiht/xY0LRdz6Fe78G6dtFtp64MLhnHu59x4/FVyBZX0bpaXlldW18nplY3Nre8fc3WurJJOUtWgiEtn1iWKCx6wFHATrppKRyBes4w+vJn7njknFk/gWRilzIzKIecApAS155kHgORAyILWHE+xIPgiBSJnc45FnVq26NQVeJHZBqqhA0zO/nH5Cs4jFQAVRqmdbKbg5kcCpYOOKkymWEjokA9bTNCYRU24+fWGMj7XSx0EidcWAp+rviZxESo0iX3dGBEI1703E/7xeBsGFm/M4zYDFdLYoyASGBE/ywH0uGQUx0oRQyfWtmIZEEgo6tYoOwZ5/eZG0T+u2VbdvzqqNyyKOMjpER6iGbHSOGugaNVELUfSIntErejOejBfj3fiYtZaMYmYf/YHx+QPDc5cD</latexit>

Model

Parameters

Training (approximates the goal over training data):

Goal

✓⇤ = argmin✓

ED [L (f✓(x), y)]<latexit sha1_base64="Nhfau74CPzVJyPBXfLHwayNIkiQ=">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</latexit><latexit sha1_base64="Nhfau74CPzVJyPBXfLHwayNIkiQ=">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</latexit><latexit sha1_base64="Nhfau74CPzVJyPBXfLHwayNIkiQ=">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</latexit><latexit sha1_base64="Nhfau74CPzVJyPBXfLHwayNIkiQ=">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</latexit>

Loss

Over all future data

✓̂ = argmin✓

1

n

nX

i=1

L (f✓(xi), yi)<latexit sha1_base64="1nEudtxg0hj2bQCWcBS1v3yjo6I=">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</latexit><latexit sha1_base64="1nEudtxg0hj2bQCWcBS1v3yjo6I=">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</latexit><latexit sha1_base64="1nEudtxg0hj2bQCWcBS1v3yjo6I=">AAACQnicbZBPaxQxGMYztWpd/6167CV0EbYgy0wR9FIoevHgoQW3rWzW4Z3sOzuhSWZI3hGXYT6bFz+BNz+AFw+W4tVDs7tzqK0PBB6e531J8ssqrTzF8Y9o49bm7Tt3t+717j94+Ohx/8nTY1/WTuJYlrp0pxl41MrimBRpPK0cgsk0nmRnb5f9yWd0XpX2Ay0qnBqYW5UrCRSitP9RFECNoAIJWr7PBbg5F0bZdJ1xkTuQTdI2thW+Nmmj9pP2k+Xvhcachnk3N/ySqt0XnC9SJZyaF7Sb9gfxKF6J3zRJZwas02Ha/y5mpawNWpIavJ8kcUXTBhwpqbHtidpjBfIM5jgJ1oJBP21WCFr+PCQznpcuHEt8lV7daMB4vzBZmDRAhb/eLcP/dZOa8tfTRtmqJrRyfVFea04lX/LkM+VQkl4EA9Kp8FYuCwjMKFDvBQjJ9S/fNMd7oyQeJUcvBwdvOhxbbJvtsCFL2Ct2wN6xQzZmkn1lP9lvdh59i35FF9Gf9ehG1O08Y/8o+nsJv0Cw0Q==</latexit><latexit sha1_base64="1nEudtxg0hj2bQCWcBS1v3yjo6I=">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</latexit>

Last Time

Page 7: Deep Learning Part 2 Other networks · Slides by: Joseph E. Gonzalez, jegonzal@berkeley.edu Deep Learning Part 2 Other networks

Learning without Labels

Ø Can we learn what inputs look like?Ø Useful inductive bias when training for a

later supervised task.Ø Often done when labeled data is

available but limited

Ø Convert to a supervised learningproblem:

Input Output

X1 Y1

X2 Y2

… …

Xn Yn

cat

baby

Data

baby

Page 8: Deep Learning Part 2 Other networks · Slides by: Joseph E. Gonzalez, jegonzal@berkeley.edu Deep Learning Part 2 Other networks

available but limited

Ø Convert to a supervised learningproblem:

Input Output

X1 Y1

X2 Y2

… …

Xn Yn

cat

baby

Data

baby InformationBottleneck

Page 9: Deep Learning Part 2 Other networks · Slides by: Joseph E. Gonzalez, jegonzal@berkeley.edu Deep Learning Part 2 Other networks

available but limited

Ø Convert to a supervised learningproblem:Input Output

X1 Y1

X2 Y2

… …

Xn Yn

cat

baby

Data

baby

InformationBottleneck

Page 10: Deep Learning Part 2 Other networks · Slides by: Joseph E. Gonzalez, jegonzal@berkeley.edu Deep Learning Part 2 Other networks

Xor Perceptrons

Page 11: Deep Learning Part 2 Other networks · Slides by: Joseph E. Gonzalez, jegonzal@berkeley.edu Deep Learning Part 2 Other networks

Perceptron Not Gate

x

-1

1

y

1

-1

Perceptron

Page 12: Deep Learning Part 2 Other networks · Slides by: Joseph E. Gonzalez, jegonzal@berkeley.edu Deep Learning Part 2 Other networks

And Gate Perceptron

Perceptron y

-1

-1

-1

1

x1 x2-1 -1

-1 1

1 -1

1 1

Page 13: Deep Learning Part 2 Other networks · Slides by: Joseph E. Gonzalez, jegonzal@berkeley.edu Deep Learning Part 2 Other networks

Or Gate Perceptron

Perceptron y

-1111

x1 x2-1 -1-1 11 -11 1

Page 14: Deep Learning Part 2 Other networks · Slides by: Joseph E. Gonzalez, jegonzal@berkeley.edu Deep Learning Part 2 Other networks

XOr Gate Perceptron

Perceptron y

-111-1

x1 x2-1 -1-1 11 -11 1

Page 15: Deep Learning Part 2 Other networks · Slides by: Joseph E. Gonzalez, jegonzal@berkeley.edu Deep Learning Part 2 Other networks

Not+1 -1

And

+1

-1

-1

-1

+1

-1

+1

+1

-1

-1

+1

+1

Or XOr

No separating hyperplane

Page 16: Deep Learning Part 2 Other networks · Slides by: Joseph E. Gonzalez, jegonzal@berkeley.edu Deep Learning Part 2 Other networks

Using one hidden layer

1

-1

1

-1-1

-1

1

1 0

y

-1

1

1

-1

x1 x2-1 -1

-1 1

1 -1

1 1


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