neural netowrks and deep learning
神经网络的机器学习，主要分为几个部分
神经网络识别手写
two important types of artificial neuron (the perceptron and the sigmoid neuron)
$$ output = \begin{cases} 0, & \text{if } \sum_{j}w_{j}x_{j} \leq threshold \ 1, & \text{if } \sum_{j}w_{j}x_{j} > threshold \end{cases} $$
simplify the describe perceptrons
$$ w \cdot x \equiv \sum_{j}w_{j}x_{j} \ b \equiv -threshold $$
$$ output = \begin{cases} 0, & \text{if } w \cdot x +b \leq 0 \ 1, & \text{if } w \cdot x +b > 0 \end {cases} $$
While the design of the input and output layers of a neural network is often straightforward, there can be quite an art to the design of the hidden layers
multilayer perceptrons orMLPs
feedforward neural networks
RNN networks, feedback loops are possible
two problems:
- breaking to segment, segmentation problem
- classifying individual digits
There are many approaches to solving the segmentation problem.
One approach is to trial many different ways of segmenting the image, using the individual digit classifier to score each trial segmentation. A trial segmentation gets a high score if the individual digit classifier is confident of its classification in all segments, and a low score if the classifier is having a lot of trouble in one or more segments.
The idea is that if the classifier is having trouble somewhere, then it’s probably having trouble because the segmentation has been chosen incorrectly. This idea and other variations can be used to solve the segmentation problem quite well.
10 outputs vs 4 outputs
gradient descent $$ C(w,b) \equiv \frac{1}{2n}\sum_{x} \begin{Vmatrix} y(x)-a \end{Vmatrix}^2 $$
ball-rolling analogy $$ \Delta C \approx \frac{\partial C}{\partial v1} \Delta v1 + \frac{\partial C}{\partial v2} \Delta v2 $$
$$ a’ = \sigma(wa + b) $$