this week: discuss how to handle events in user-interface — MVC, functional reactive programming.

Lecture 4.1 - Imperative Event Handling: The Observer Pattern

Traditional way of handling events: observer Pattern (MVC). Used when views need to react to change in a model.

MVC: model-view-controller for user interface

• Views can announce themselves ...

This week: scala for imperative programming.

Lecture 3.1 - Functions and State

So far: pure functional programming
→ side-effect free: therefore time doesn't matter.
Any rewriting that terminates lead to the same solution. (Churcher-Rosser Th)

Now: mutable states

Stateful objects: objects can have state that change over time. (state is ...

1. BASIC DATA TYPES: int, bool

Rich type system and polymorphism in ocaml. Types are infered not declared.

Basic types: int, bool, float, string, char, ...

int

value: \(-2^{62}\) ~ \(2^{62}-1\) on 64-bit machines.
ops: +, -, *, /, mod (reminder: / is integer division)

# 3+2*4-1;; 
- : int = 10 
# 5/2;; 
- : int ...

Lecture 2.1 - Structural Induction on Trees (optional)

Generalize the structural induction on list to general structures like trees.

To prove a property P(t) for all trees t:
* show for any leave l, P(l) holds
* for each internal node t with subtrees s1...sn, show P(s1)&...&P ...

上次捣鼓pelican博客系统还是在2012年, 那时farseerfc学长就提供了无私的帮助, 当时非常兴奋写了这么一篇简单的教程.

这两天捣鼓了好久 终于把博客升级到了pelican3...

新的pelican貌似希望使用者把写作的内容和生成的网页分成两个repo管理, 我嫌麻烦还是把它们都放在了一个repo下面: https://github.com/X-Wei/x-wei.github.com 这个repo包含了生成的网页以及我写作的内容(在pelican_dir目录下面).

换了超赞的新皮肤, 这个是farseerfc学长定制的bootstrap3主题, 非常精美. 学长的版本包含了繁简英日翻译以及导出pdf/png什么的按钮, 功能非常全, 不过直接拿来用不太合适, 比如我就不需要博客的日语版... 我实际是用的silverchard对farseerfc学长主题的修改版. 再稍微修改了一下配色什么的...

如果想要做一个类似的博客, 下面是一些步骤:

1. 安装软件

首先安装pelican3以及其他一些python module (另外个人建议新建一个virtualenv在里面搞):

pip install pelican jinja2 py3babel babel beautifulsoup4 markdown ...

ref: http://rnduja.github.io/2015/10/26/deep_learning_with_torch_step_7_optim/
doc: https://github.com/torch/optim/blob/master/doc/intro.md

Before we implement the gd update step by defining a gradientUpdate function and calling it in a loop.

function gradientUpdate(model, x, y, criterion, learningRate) 
    local pred = model:forward(x ...

ref: http://rnduja.github.io/2015/10/07/deep_learning_with_torch_step_4_nngraph/
doc: https://github.com/torch/nngraph/

The aim of this library is to provide users of nn package with tools to easily create complicated architectures.

luarocks install nngraph

optionally can install graphvis for graph visualization.

From previous posts, to build networks ...

ref: http://rnduja.github.io/2015/10/05/deep_learning_with_torch_step_3_nn_criterions/
doc: https://github.com/torch/nn/blob/master/doc/criterion.md

Criterion: abstract class, given input and target(true label), a Criterion can compute the gradient according to a certain loss function.

Criterion class

important methods:

• forward(input, target): compute the ...

doc: https://github.com/torch/nn/blob/master/doc/containers.md
ref: http://rnduja.github.io/2015/10/04/deep_learning_with_torch_step_2_nn_containers/

Container, similarly to Module, is the abstract class defining the base methods inherited from concrete containers. Container contains modules (layers).

Container class

important methods:

• add(module)
• get(index): get module ...

Module is an abstract class which defines fundamental methods necessary for a Layer.

doc: https://github.com/torch/nn/blob/master/doc/module.md

Module class

variables in Module:

• output: Tensor, the ouput computed from last call of forward(input)
• gradInput: Tensor, gradient wrt input of module, computed from last ...