introduction to deep learning
play

Introduction to Deep Learning by Boris Hanin June 12, 2020 - PowerPoint PPT Presentation

T exas A&M Institute of Data Science Tutorial Workshop Series Introduction to Deep Learning by Boris Hanin June 12, 2020 Deeplearninglutorial :c : :c :::i:i 0p obtain D to Use : NN ? \* set : What setting *


  1. T exas A&M Institute of Data Science Tutorial Workshop Series Introduction to Deep Learning by Boris Hanin June 12, 2020

  2. Deeplearninglutorial :c : :c :::÷÷÷i÷:÷÷¥i ③ 0p¥ obtain D to Use : NN ? \* set : ① What setting ① * : God is a . x llcscaj -0*7 f Coca ) ? NNS used ② How are ° e.g linear regression are made by of choice kinds What . ③ engineers ? ④ Testy Q* well See how : data : modes ? performs failure and unseen ④ Main on use cases a UGG 0*5 ? I - Has Supervised Learning - - ① DataAcqoisio salient dataset Features obtain : " " . :c : : " ÷ : ② todeseection : choose model both interpolate and Nws . xi → MCKIE ) extrapolate of vector where ⑦ param s = McKie ) - e. g , O - Cai ) . . tanka -_ a , Kit - . .

  3. :/ Neutrals Max { 0 , t } Reluct ) Ey : : oct ) = = are built of " " • Neural nets . soto ) neurons off sczscx ) - yd ) o y ⑨ 2- ( xjo ) = 6 ( bt - 4rCx - tknwn ) ✓ I x= ( x ; - pen ) ' 494 ' → . . . . •¥ E- ( ) g by w • weights x bias miiiiiieiiiii ÷÷ iii. : • rc.io#-/l.. A neural network is collection " Def " a • and wiringdia-g.am of neurons T . W 's ) " architecture = ( all b 's " ⑦ " ✓ ✓ ' O Xz• - - f • 2 her

  4. " layers ↳ ↳ ° In practice I :O ) NC have " NNS x : ÷¥¥¥::÷ • I : descent on E by = # layers gradient ③ optimize 2 - racial e .cCo7=lfGd fool .CO ) • ; Logo ) • 1st layer input 2nd layer ) ) Coc , Ha ④ Testing Draw new : ad " secs ) urge ; whether i → x check and • layers /N(xi0*)xfG heirarchical reps to in " no , > 1 Typical : better * D= fish .fm } Empirically deeper is ① Datsun : : ② Architecture : choose o 's , depth , width wiring diagram , - { W , b } ③ Randomly initialize O

  5. human I . flaw ) { to has Maines : ' ch . = o ( w , ① NL P " the Y * - Driving big cat is . Self Cars • sq . = " le est grand " Rec chat Facial f- Gcn ) - o . Learning • Google ③ Reinforcement Translate system of state • och - Siri ) - - board of chess position ( e.g . Bots - Chat action reward often ) - masc - - ③ Vision move ) Computer best next ( e.g , = image • och • AlphaGo ' Exploration AU by N

  6. IBI ? 7 NeuralNetoptimization-foo-fo.qoe.co choose How to okey : ) , but accurate slow : ① small Intuition 2. as W • • • ¥ : - . I but noisy → fast large A < ' I ' params ? for all . # 2 • ② why choose same " \ x . sensitive to rise ; o ) might be very 02 not to learning rate but wage ) E , . by grid search d ③ In practice - 2W Io find 1 : • GD : SW = OW log - space - on " back propagation constant during train ? • Compute }Iw " ④ why keep a using ¥÷÷÷÷÷÷÷:÷ : : : : " :÷÷÷÷÷÷÷÷÷ ÷ . . . bastitgeh That , feel .CO ) a Lo but fast as noisy IBI ⑤ small slow as accurate but CBI large [ 2 . IBI # const ) • god o small batches mean : are inversely related computation less ⑥ I , IBI

  7. Architecture Selection : × , yw - o ) . I NGS g \ - is always data - dependent • o - J • Best architecture o . : o - • # , { Kz \ based Tran former - / 00 • µ , p ← CLS -1Mt Attention ) Recurrent . - -_ W . Nz Wh . Nz - Er Er .EE I - convolutional , Residual ow • cuts ow . - Jacobi ans x # layers product choices : Still leaves • many 181W I ( width depth . O • details of + a = or : wiring . " exploding " ( = Re LV ) 1 vanishing gradients of • choice r initialize and to to how u optimize * • Empirical good deep is : * often but less stable =

  8. order ÷#• Cmu Net Residual Network . In layer has every : a - structure channels xcxiy ) # ① I ° Every layer shares in - raft - neuron - - ④ - or Nz x , weights addition ! ! i : j ! ! + valour , Gcs ) , Gc ) - actor output correction to /•¥ € ^•0 • co • t . . . the Intuition : Nj g- . # . . = # • oo# . . Krs x ✓ . inputs RGB ' etso . are c nxn r a ④ / , Key : Images C . l c l l . . go y ( ' c c / ] :O : : • • anerierarc-h.ae : ' I . i 1st and 104 all B G R neuro so ROB look for same pattern • channels

  9. 89.9T . -7 99.9 's Challenges I hydrant . - IF Cases New I STOP I ① Use : 1--1 , physics , chemical ) • PDE ( fluids I . . . • Biology ( genomics ) Shift Distribution ② change ) of data ( nature : → change hardware in cloudy sunny vs • . tend be to NNS issue • : brittle

Download Presentation
Download Policy: The content available on the website is offered to you 'AS IS' for your personal information and use only. It cannot be commercialized, licensed, or distributed on other websites without prior consent from the author. To download a presentation, simply click this link. If you encounter any difficulties during the download process, it's possible that the publisher has removed the file from their server.

Recommend


More recommend