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Take-Home Point 1

Deep Learning is nothing more than compositions of functions on matrices.

- +

Take-Home Point 2

Bayesian deep learning is nothing more than learning a distribution for each parameter rather than a point estimate.

- +
@@ -69,19 +69,19 @@

Linear Regression

Function

- +

Matrices

- +

Neural Diagram

- +

LinReg 3 Ways

- +
@@ -90,19 +90,19 @@

Logistic Regression

Function

- +

Matrices

- +

Neural Diagram

- +

LogReg 3 Ways

- +
@@ -111,19 +111,19 @@

Deep Neural Networks

Function

- +

Matrices

- +

Neural Diagram

- +

DeepNets 3 Ways

- +
@@ -141,15 +141,15 @@

Bayesian Linear Regression

Intuition

- +

From this...

- +

..to this

- +
@@ -158,11 +158,11 @@

Bayesian Logistic Regression

From this...

- +

...to this

- +
@@ -171,16 +171,16 @@

Bayesian Deep Nets

From this...

- +

...to this

- +

Cheat Sheet

- +
@@ -197,7 +197,7 @@

Cheat Sheet

Predict Forest Cover Type

Network Architecture

- +
@@ -241,15 +241,15 @@ 

Network Architecture

1st Layer Weights

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2nd Layer Weights

- +

Output Weights

- +
@@ -277,12 +277,12 @@

Take-Home Point 1

Deep Learning is nothing more than compositions of functions on matrices.

- +

Take-Home Point 2

Bayesian deep learning is nothing more than learning a distribution for each parameter rather than a point estimate.

- +

Thank you!

@@ -290,7 +290,6 @@

Thank you!

-