ML Coursera 4  Week 4: Neural Networks  Representation
Posted on 16/09/2018, in Machine Learning.This note was first taken when I learnt the machine learning course on Coursera.
Lectures in this week: Lecture 8.
Neural Networks
Check [this link](https://medium.com/datathings/neuralnetworksandbackpropagationexplainedinasimplewayf540a3611f5e)
to see the basic idea of NN and forward/backward propagation.
 Algorithms try to mimic the brain.
 80s, 90s (very old)
 activation function = sigmoid (logistic) $g(z)$
 (sometimes) $\theta$ called weights parameters

Neural network: first layer (input layer), intermidiate layers (hidden layer) and the last layer (output layer)
 Notations:

$a_i^{(j)}$ = “activation” of unit $i$ in layer $j$
$$ a_{\text{unit}}^{(\text{layer})} $$  $\Theta^{(j)}$ = matrix of weights controlling function mapping from layer $j$ to layer $j+1$

$\Theta \in \mathbb{R}^{m\times n+1}$ where $m$ = number of unit in current layer, $n$ = number of units in previous layer (not include unit 0).
$$ \begin{align} \Theta^{(\text{previous})} &: \text{previous layer} \to \text{current layer} \\ \Theta^{(\text{previous})} &\in \mathbb{R}^{\text{current} \times (\text{previous}+1)} \end{align} $$$$ a_{k\in \text{current units}}^{(\text{current layer})} = g\left( \sum_{i\in \text{prev units}} \Theta_{ki}^{(\text{prev layer})} a_i^{(\text{prev layer})} \right) $$

Forward propagation: vectorized implementation
 Neural network is look like logistic regression, except that instead of using $x_1, x_2, \ldots$, it’s using $a^{(j)}_1, a^{(j)}_2, \ldots$
 Neural network learning its own features.
 Architectures = how different neurons connected to each other.

Final
$$ \begin{align} h_{\theta}(x) = a^{(j+1)} = g(z^{(j+1)}) = g(\Theta^{(j)}a^{(j)}). \end{align} $$
Examples and Intuitions I
Examples and Intuitions II
Multiclass classification
Exercise Programmation: Multiclass Classification and Neural Networks

lrCostFunction.m
This ex is the same with the one in the previous week.
h = sigmoid(X*theta); % hypothesis J = 1/m * ( y' * log(h)  (1y)' * log(1h) ) + lambda/(2*m) * sum(theta(2:end).^2); grad(1,1) = 1/m * X(:,1)' * (hy); grad(2:end,1) = 1/m * X(:,2:end)' * (hy) + lambda/m * theta(2:end,1);

oneVsAll.m
initial_theta = zeros(n + 1, 1); options = optimset('GradObj', 'on', 'MaxIter', 50); for c = 1:num_labels [theta] = fmincg (@(t)(lrCostFunction(t, X, (y == c), lambda)), initial_theta, options); all_theta(c,:) = theta(:); end
info
fmincg
works similarly tofminunc
, but is more more efficient for dealing with a large number of parameters. 
predictOneVsAll.m
h = X * all_theta'; [~, p] = max(h,[],2);

predict.m
: “you implemented multiclass logistic regression to recognize handwritten digits. However, logistic regression cannot form more complex hypotheses as it is only a linear classifier.”X = [ones(m, 1) X]; % add column 1 to a2 z2 = X * Theta1'; a2 = sigmoid(z2); a2 = [ones(m, 1) a2]; % add column 1 to a2 h = a2 * Theta2'; [~, p] = max(h,[],2);