Texas A&M Institute of Data Science Tutorial Workshop Series

Introduction to Deep Learning

by Boris Hanin

June 12, 2020

Deeplearninglutorial ③ 0p¥ : Use D to obtain

setting ① * set .:God : ① What is a NN ?

\* f Coca) x llcscaj -0*7② How NNS are used ?

③ What kinds of choice are made by°

e.g .

linear regression

engineers ? ④ Testy : See how well Q*

④ Main use cases and failure modes? performs on unseen data :

- I Has a UGG 0*5 ?Supervised Learning--

① DataAcqoisio obtain dataset salient Features :

÷:":c: :c:::÷÷÷i÷:÷÷¥i .:c : :""② todeseection : choose model

. Nws both interpolate and

xi→ MCKIE) extrapolatewhere ⑦ = vector of param s

- e. g . McKie) -_ a ,Kit .. . tanka

,O -- Cai)

Neutrals : Ey : oct) = Reluct) = Max { 0 , t }• Neural nets are built of

"

neurons"

off . soto) sczscx)I ⑨ - 4rCx -yd)x= (x

; . .- pen) '→ 2- (xjo) = 6 (bt '494 . . - tknwn) ✓ oy gE- ( by w )

• •¥bias weights x

÷÷: iii.miiiiiieiiiii:/•

"

Def"

A neural network is a collection

of neurons and wiringdia-g.amT

" architecture"

⑦ = ( all b's.

W 's)

"

•

rc.io#-/l..✓ ✓ O

'-

Xz•-• 2 fher

° In practice NNS have " layers"

: x NC :O)• I

÷¥¥¥::÷:2 =# layers ③ optimize E by gradient descent on•

• Logo) fool .CO) ; e.cCo7=lfGd - racial

input 1st layer 2nd layerx ↳ ad" i→ secs) ↳ urge;

④ Testing : Draw new Coc , Ha))I and check whether

• layers to heirarchical repsTypical : in

"no ,> 1

/N(xi0*)xfG① Datsun : D= fish.fm} Empirically : deeper is better *

② Architecture : choose o's,

wiring diagram , depth , width

③ Randomly initialize O - { W , b }

Maines : . flaw) = { to .

,

'ch has human

o ( w

① NL PY *

• sq.=

"

the cat is big . Self- Driving Cars

. f- Gcn) -"

le chat est grand"

o Facial Rec

• Google Translate ③ Reinforcement Learning• och -- state of system

- Siri (e.g . position of chess board)

- Chat Bots I often) -- reward - masc action③ Computer Vision (e.g ,

best next move )• och = image

• AlphaGo' Exploration by AU

N

NeuralNetoptimization-foo-fo.qoe.co) okey : How to choose 7,

IBI ?

W •- • Intuition : ① small 2. as slow but accurate

I '.I ' large A <→ fast but noisy

x .

•¥: •

② why choose same 2 for all params ?"

\.# rise; o) might be very sensitive to

learning rate wage) E ,but not to 02 .

d• GD : SW = - 2W Io ③ In practice : find 1 by grid search-

OWon log -space

• Compute }Iw using" backpropagation

"

④ why keep a constant during train?

÷:÷÷÷÷÷÷÷÷÷. . . ¥÷÷÷÷÷÷÷:÷::::"bastitgeh That , feel.CO) a Lo ⑤ small IBI as noisy but fast

large CBI as accurate butslow

• god : o small batches mean [ 2 . IBI # const)less computation ⑥ I , IBI are inversely related

Architecture Selection :×, yw . I NGS- o)

g-

• \ o

• Best architecture is always data - dependent : o-

o- J .

•# 00

• µ ,p ←, { Tranformer -based Kz \ /Recurrent CLS -1Mt Attention)

.- .

Wh Nz Nz-_W .

I -- Er Er.EE

.• cuts convolutional

,Residual ow ow

-

product x# layersJacobians

• Still leaves many choices:

• details of wiring (width depth . . : 181W I = O or + a

• choice of r ( = ReLV) " exploding 1 vanishing gradients"

u how to initialize and to

optimize• Empirical : deep is good

*

*= but often less stable

Residual Network : . In a Cmu Net every layer has-

channels xcxiy) structure

#①I -

- -

④- or,

° Every neuron in layer sharesx - raft Nz weightsaddition

output - actor, Gc) + valour, Gcs) : j ! ! ! ! it . . . ^•0 • co •

Intuition : Nj = g-the order ÷#• . ..

#correction to /•¥€ . .Krs x #

✓• oo#

etso.

. inputs are nxn RGB a c,

'

r

. C l

④/ Key : Images .. c l l

•

go y '

c (c

/] :O :: anerierarc-h.ae : i . I • '

ROB so and 104 R G B all neuro 1st

channels look for same pattern •

Challenges 89.9T. -7 99.9 's. hydrant

-

IF① New Use Cases : I STOP I

• PDE ( fluids , physics , chemical . ..) 1--1

• Biology ( genomics)I

② Distribution Shift( nature of data change) : I→ change in hardware

• sunny vs . cloudy• issue : NNS tend to be

brittle