Deep Learning

A Primer

Feb 8th 2018

J. Horacsek

What is Deep Learning?

What is Deep Learning?

Practically, deep learning can be thought of as very deep neural networks

 

Why is it important?

Real world problems are often not well posed.

Take an image as input, does that image contain a dog?

Why is it important?

Why is it important?

Why is it important?

Why is it important?

Real world problems are often not well posed.

We'll stick to supervised learning and classifiers here.

Take an image as input, does that image contain a dog?

We have the data + labels

The data fall into classes (cats vs. dogs)

What is Deep Learning?

Practically, deep learning can be thought of as very deep neural networks

 

Why is it important?

Why is it important?

Recipe for a good deep learning algorithm

  • A good architecture
  • LOTS of data
  • LOTS of training

This isn't an exhaustive list (other research in deep learning has also contributed, dropout, activation functions, optimization, etc..)

What is Deep Learning?

Practically, deep learning can be thought of as very deep neural networks

What is Deep Learning?

What is Deep Learning?

\(N(\vec{x})\) is the "function" that represents the neural net -- \(\vec{x}\) can be an image, audio signal or text

What is Deep Learning?

What is Deep Learning?

\(f(x)\) is an activation function

What is Deep Learning?

\(f(z)\)

\(f(z)\)

\(f(z)\)

\(f(z)\)

\(f(z)\)

\(f(z)\)

ReLU: \(f(z) = H(z)z\) where \(H(z)\) is the heaviside function.

How to train networks?

Training takes a lot of data

Many \((\vec{x}_i, {y}_i)\) pairs, where \(\vec{x}_i\) is say, an image and \(y_i\) encodes the output (say, 0 for no dog, 1 for dog).

\(J(W) = \sum_i E(x_i, y_i, W)\)

Minimize error function

\(E(x,y,W) = (N(x,W)-y)^2\)

Here, \(E(x,y)\) is an error function, we could use

But there are many different error metrics

How to train networks?

Minimize via stochastic gradient descent start with \(W_0\) as a random vector

\(W_{i+1} = W_{i} - \gamma \nabla_W J(W_i) \)

Minimize error function via

Need to be able to compute \(\nabla_W N(x) \), which we can do via back propogation (i.e. the chain rule).

I'm not going to go over this here, this important, but it's more important to know that backprop=derivative.

History

Neural nets have existed since the 1980's, (perhaps even earlier) why are they so successful right now?

Training is computationally expensive, highly parallel computers and GPUs meet this need

However, architectural advancement have also been extremely important

Dense Networks

Are all these connections necessary/useful?

Dense Networks

The brain has a multitude of different cell structures

Dense Networks

The brain has a multitude of different functional "compartments"

Dense Networks

It's incredibly naive to think that dense networks would generalize well

Convolutional Networks

Instead, take another cue from biology, as well as trying to incorporate spatial locality

Small receptive fields, hierarchical representation

Convolutional Networks

Define a mask that is shifted over each pixel of the image

Weights are unknown, found using SGD.

Convolutional Nodes

Convolutional Networks

Convolutional Nodes

Convolutional Networks

Convolved feature is then passed through a non linearity (activation function, usually ReLU)

But this is still a large image, we want to look at it at multiple scales

Convolutional Networks

Max Pooling Layers

Increadibly simple idea, look at the nodes that are have highest activation in an area

Produces a lower resolution map of important features

Convolutional Networks

These additions are really what reinvigorated research in neural nets

Additional resources...

Of course not, libraries like Tensor Flow and Theano do all the hard math.

Do you need to code all this from scratch?

Other libraries like Caffe or Keras deal with NNs at a higher level (basically, you define the architecture + error metric, then feed it data)

Deep Learning

By Joshua Horacsek

Deep Learning

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