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Clarifai

Let’s reveal the mystery!

Neural Nets

What this can do!

Let’s start with a node

ArchitectureForward Pass•Calculating the loss

Backward Pass

•Backpropagation Algorithm

•Distributing Gradients

Optimizing

•Reducing the loss

•Updating the weight matrix

Updating weights

Loss FunctionUpdating weights

YOU CAN’T TRAINIF THERE ARE NO GRADIENTS

This went deeper!How ?With the help of two superheros!

Deep Neural Networks

What makes this special?

Hierarchical Feature Representation

Why this feature thing is so effective ?

Then what happened ?Deep Neural Nets became harder and harder to train!

DEEP NETS!

Y U NO EASY?

Number Of Parameters?

These nets are huge!

Millions of layers Millions of nodesBillions of parameters

BUT DON’T KNOW HOW MANY HIDDEN LAYERS / NODES TO

USE?

WHEN YOU WANT TO BUILD A NEURAL NET

Overfitting

Killing the training process

Neuron in a neural network

Sigmoid activation function

Too much connections ? What if we input some exact features!

Feature Engineering

Canny edge detection filter

How we see things ?

Important!

Not all pixels!

But patterns of pixels!

This is fast!

Right to left ?

Can you remember

NOT EVERYTHING

BUT IMPORTANT THINGS!

Can we design set of features for machines ??

No way!We may design some high level features!

But our machines deal with PIXELS!

What if we let the machine to extract it’s own features!

Wow that was nice :)

BRACE YOURSELVESCNN IS COMING

Imagenet Challenge

Step by step

ConvNets have one layer called CN layer after the input layer

This act as an automatic feature extractor

Additional Layers!Convolution layer

RELU layer (Nonlinearity)

Max pooling layer

Amazing local connectivity

Let’s go layerwise!

Convolutional Layer This is simply applying convolution to input pixels

Now! What is convolution?

Mathematical way

Easy way!

For an image

CONVOLUTION

EVERYWHERE

More generally...

Filter search for things in the image

When filter sees something similar to it’s orientation in an image it will fire up!

Image get transformed into something new !

We call this new image as a feature mapBecause it mapps some features from the original

image w.r.t to the filter!

Right - Feature map obtained by applying canny edge filter on an image

It’s not only a single filter!

Many filters searching for diffrent things!

How amazing!

So we will have different feature maps!

Simply each filter has it’s own task

Gabor filters are similar to those of the human visual system, and they have been found to be particularly appropriate for texture

representation and discrimination.

How filter weights looking at an image!

This is how it’s done!

Feature maps = Set of neurones

That’s convolution layer!But where’s the magic ?

All these filters are trainable!

Means ?

These are not man made filters! Look at the values of following filter kernels

What about the filter?

We can design size , channels of the filter

But not its values!

Each filter values is trainable weight

All in one!So every filter values is trainable

It’s like a set of weights !

But how ?

With backprop

Convolution is a perfectly differentiable function!

So we can learn it’s weight parameters

Reminder!

Then we apply some nonlinearity We apply rely on each point in the feature maps

Remember : These points in the feature maps are like neurons

This is like just forgetting the negative parts of the

So the orientation be like

Then the pooling layer

This is like a filter without parameters!

This is for subsample or to reduce the dimensions of the feature maps

Simply this will extract the most important features from the feature map

That’s basics! Lenet

So finally….

We can train these filter weights using backprop and some kind of optimization algorithms(Adam)

Here’s a such view of a trained filter

Motivation!

Q & A !