introduction to deep learning - geek night (may'16)
TRANSCRIPT
Deep LearningAn Introduction to
with Tensorflow
Manish M ShivanandhanProduct Manager, Adappt
PatternsAnd the world is full of it!
F(X) = Y = X^2
Perfect DatasetX 0 1 2 3 4 5
Y 0 1 4 9 16 25
F(6) = 36
F(X) = Y = X^2
Imperfect DatasetX 0 1 2 3 4 5
Y 1 1 4 8.5 16 24
F(6) = 36Error = e%
Z = X+Y
Multiple InputsX 0 1 2 3 4 5
Y 0 1 2 3 4 5
Z 0 2 4 6 8 10
F(10,20) = 30
Fitting the Line
Learning Types
SupervisedWith Labels
Eg. Spam filtering, Face recognition
UnSupervisedWithout Labels
Eg. Google News
ReinforcementDynamic Labels
Eg. most of the supervised learning problems
Prediction Types
ClassificationDiscrete set of outputs
Eg. alphabets, numbers, boolean
RegressionContinuous set of outputs
Eg. stock prices
Deep LearningWhy?
AbstractionHigher Levels of
FrameworksTheano, Tensorflow, Caffe
GPUs
Thanks Gamers!
Why Tensorflow? ●Data flow graph●Easy to use●Backed by Google
Resources●Tensorflow.com●Learningtensorflow.com●Udacity’s introduction to Deep learning
Questions?
Thank [email protected]