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f(numerical) = classification
Fr a u d d e t e c t i o nC r e d i t s c o r i n g
C h u r n p r e d i c t i o nR e s p o n s e m o d e l s* S p a m d e t e c t i o n
CLASSIC EXAMPLES
f(text) = intent
“q u e s t i o n a n s w e r i n g ”/ “ i n t e n t m a t c h i n g ”
SIRI / ALEXA
f(intent) = answer
f(audio) = text
“ s p e e c h t o t ex t ”
f(text) = audio
“ t ex t t o s p e e c h”
f(image) = [objects]
G o o g l e P h o t o sF a c i a l R e c o g n i t i o n
IMAGE OBJECT DETECTION
f(photo) = text
IMAGE CAPTIONING
https://research.googleblog.com/2014/11/a-picture-is-worth-thousand-coherent.html
f(photo) = painting
IMAGE STYLE TRANSFER
P R I S M AN B : N O I N P U T/O U T P U T PA I R S ! ? !
https://github.com/junyanz/CycleGAN
f(horse) = zebra?
VIDEO STYLE TRANSFER
https://github.com/junyanz/CycleGAN/blob/master/imgs/horse2zebra.gif
f(game state) = action
ATA R IS t a r C ra f tA l p h a G o
DEEP MIND
https://www.youtube.com/watch?v=Q70ulPJW3Gk
1 . T h e b a s i c s 2 . S u p e r v i s e d v s U n s u p e r v i s e d3 . S u p e r v i s e d : M u l t i v a r i a t e & L o g i s t i c R e g r e s s i o n4 . F e a t u r e E n g i n e e r i n g5 . U n s u p e r v i s e d : C l u s t e r i n g 6 . S u p e r v i s e d : S V M s & R a n d o m F o r e s t s7 . U n s u p e r v i s e d : P r i n c i p a l C o m p o n e n t s ( e i g e n f a c e s )8 . S u p e r v i s e d : R e c o m m e n d e r S y s t e m s9 . R e i n f o r c e m e n t L e a r n i n g & G e n e t i c A l g o r i t h m s1 0 . S u p e r v i s e d : N e u r a l N e t w o r k s & D e e p L e a r n i n g1 1 . S t a t e o f t h e a r t : N L P 1 2 . S t a t e o f t h e a r t : C o m p u t e r V i s i o n1 3 . Tr a n s f e r L e a r n i n g & F i n e - t u n i n g1 4 . P r a c t i c a l T i p s1 5 . I m p l e m e n t a t i o n s ( G o a c t u a l l y b u i l d s o m e t h i n g ! )1 6 . W h e r e t o f r o m h e r e
OUTLINE
https://www.youtube.com/watch?v=kwt6XEh7U3g
RANDOM FORESTSGreat explanation by Jeremy Howard (previously of Kaggle, now fast.ai)
CONVOLUTIONAL NEURAL NETWORKS
http://cs231n.github.io/convolutional-networks/
https://ujjwalkarn.me/2016/08/11/intuitive-explanation-convnets/
SOME TIPS
1. Handling missing values
2. Pseudo labelling
3. Ensambles always win
4. Get (or generate) more data
MOOCSh t t p s : // w w w . c o u r s e r a . o r g / l e a r n /m a c h i n e - l e a r n i n gh t t p : // w w w . f a s t . a i
BOOKSh t t p : // w w w - b c f. u s c . e d u / ~ g a r e t h / I S L /
COMMUNIT IESh t t p s : // w w w . k a g g l e . c o m /h t t p s : // w w w . r e d d i t . c o m /r /m a c h i n e l e a r n i n g
PODCASTSh t t p s : / / w w w . o r e i l l y . c o m / t o p i c s / o r e i l l y - d a t a - s h o w - p o d c a s t
h t t p : // w w w . t h e t a l k i n g m a c h i n e s . c o m /