Neural Networks (Geoffrey Hinton Course)

Some Simple Models or Neurons

$y$ output, $x_i$ input.

Linear Neurons

$y = b + \sum_{i} x_i w_i$

$w_i$ weights, $b$ bias

Binary Threshold Neurons

$z = \sum_{i} x_i w_i$

$y = 1$ if $z \geq \theta$, $0$ otherwise.

Or, equivalently,

$z = b + \sum_{i} x_i w_i$

$y = 1$ if $z \geq 0$, $0$ otherwise.

Rectified Linear Neurons

$z = b + \sum_{i} x_i w_i$

$y = z$ if $z > 0$, $0$ otherwise. (linear above zero, decision at zero.)

Sigmoid Neurons

Give a real-valued output that is a smooth and bounded function of their total input.

$z = b + \sum_{i} x_i w_i$

$y = \frac{1}{1 + e^{-z}}$

Stochastic Binary Neurons

Same equations as logistic units, but outputs $1$ (=spike) or $0$ randomly based on the probability. They treat the output of the logistic as the probability of producing a spike in a short time window.

$z = b + \sum_{i} x_i w_i$

$P(s = 1) = \frac{1}{1 + e^{-z}}$

We can do a similar trick for rectified linear units - in this case the output is treated as the Poisson rate for spikes.

Types of Learning

Supervised Learning

Learn to predict an output when given an input vector.

• Regression: The target output is a real number or a whole vector of real numbers.
• Classification: The target output is a class label.

How supervised Learning Typically Works

1. Start by choosing a model-class: $y = f(x;W)$
   • A model-class $f$ is a way of using some numerical parameters $W$ to map each input vector $x$ into a predicted output $y$.
2. Learning usually means adjusting the parameters to reduce the discrepancy between the target output, $t$, on each training case and the actual output, $y$, produced by the model.
   • For regression $\frac{1}{2}(y-t)^2$ is often a sensible measure of the discrepancy.

Reinforcement Learning

Learn to select an action to maximize payoff.

Unsupervised Learning

Discover a good internal representation of the input.