In [1]:

```
# %load /Users/facaiyan/Study/book_notes/preconfig.py
%matplotlib inline
import matplotlib.pyplot as plt
import seaborn as sns
sns.set(color_codes=True)
plt.rcParams['axes.grid'] = False
import numpy as np
#from IPython.display import SVG
def show_image(filename, figsize=None, res_dir=True):
if figsize:
plt.figure(figsize=figsize)
if res_dir:
filename = './res/{}'.format(filename)
plt.imshow(plt.imread(filename))
```

feedforward: no feedback connections.

input layer -> hidden layers -> output layer

\begin{align} y &= f(x; \theta) \approx f^*(x) \\ &= W^T \phi(x) + b \end{align}how to choose the mapping $\phi$?

- use a very generic $\phi$, such as RBF kernel $\implies$ generation remains poor.
- manually engineer $\phi$ $\implies$ classical feature engineer.
- learn $\phi$ by deep learning. $y = f(x; \theta, w) = \phi(x; \theta)^T w$.
- advantage: only needs to find the right general function family
*VS*find the right function.

- advantage: only needs to find the right general function family

$h = g(W^T x + c)$, where an affine transformation followed by an activation function $g$.

In [2]:

```
show_image("fig6_2.png", figsize=(5, 8))
```

default activation function is rectified linear unit (ReLU): $g(z) = max\{0, z\}$

- advantage:
- piecewise linear function: very close to linear.
- easy to optimize with gradient-base methods.

In [3]:

```
relu = lambda x: np.maximum(0, x)
x = np.linspace(-2, 2, 1000)
y = relu(x)
plt.ylim([-1, 3])
plt.grid(True)
plt.plot(x, y)
```

Out[3]:

important:

- initialize all weights to small random values.
- initialize biases to zero or small positive values (push result to right area of ReLU).

In most cases,

- our parametric model defines a distribution $p(y | x; \theta)$,
- simply use the priciple of maximum likelihood.

$\implies$ cross-entropy as the cost function.

maximum likelihdood in neural networks => cost function is simply the negative log-likelihood == cross-entropy.

\begin{equation} J(\theta) = - \mathbb{E}_{x, y \sim \hat{p}_{data}} \log p_{model}(y | x) \end{equation}advantage:

- Specifying a model p(y | x) automatically determines a cost function log p(y | x). => removes the burden of designing cost functions for each model.
- Undo the exp of some output units => avoid staturate problem (flat area, very small gradient).

unusual property of the cross-entropy cose: does not have a minimum value (negative infinity). => regularization.

cost L2: learn mean of y when x is given.

cost L1: learn median of y when x is given.

Linear output layers are often used to produce the mean of a conditional Gaussian distribution.

这里意思是说，给定$x$，它对应的样本集$y$应是高斯分布。而用线性模型来学习，预测的正是样本集均值$f(x) = \bar{y}$。可见，这种情况常见于回归问题。

binary classification

\begin{align} P(y) &= \delta((2y - 1) z) \quad \text{where } z = w^T h + b \\ J(\theta) &= - \log P(y | x) \quad \text{undo exp} \\ &= \zeta ((1 - 2y) z) \end{align}maximum likelihood is almost always the preferred approach to training sigmoid output units.

multiple classification

\begin{equation} \operatorname{softmax}(z)_i = \frac{\exp(z_i)}{\sum_j \exp(z_j)} \end{equation}\begin{align} \log \operatorname{softmax}(z)_i &= z_i - log \sum_j \exp(z_j) \\ & \approx z_i - \max_j (z_j) \end{align}Overall, unregularized maximum likelihood will drive the softmax to predict the fraction of counts of counts of each outcome observed in the training set.

The argument $z$ can be produced in two different ways:

- one vs rest: N estimators
- choose one class as "pivot" class: N - 1 estimators

softmax provides a "softened" version of the argmax.

In general, think neural network as representing a function $f(x; \thetha) = w$, which provides the parameters for a distribution over $y$. Our loss function can then be inperpreted as $- \log p(y; w(x))$.

- Rectified linear units are an excellent default choice of hidden unit.
- In practice, gradient descent still performs well enough for functions which are not actually differentiable.

The widespread saturation of sigmoidal units => use as hidden units is now discouraged.

- layers: group of units
- chain structure
- the depth of the network
- the width of each layer

universal approximation theorem: large MLP will be able to **represent** any function. However, we are not guaranteed that the training algorithm will be able to **learn** that function.

Empirically, greater depth does seem to result in better generalization.

chain rule:

\begin{equation} \frac{dz}{dx} = \frac{dz}{dy} \frac{dy}{dx} \end{equation}In [ ]:

```
```