[learning torch] 4. Criterion (loss function)

Sat, 08 Oct 2016 Category notes torch Series Part 4 of «torch学习笔记»

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 …

[learning torch] 3. Container (models)

Fri, 07 Oct 2016 Category notes torch Series Part 3 of «torch学习笔记»

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 …

[learning torch] 2. Module (layers)

Fri, 07 Oct 2016 Category notes torch Series Part 2 of «torch学习笔记»

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 …

[learning torch] 1. Tensor

Fri, 07 Oct 2016 Category notes torch Series Part 1 of «torch学习笔记»

A Tensor is the fondamental data type in torch, (similar to numpy for tensorflow), it's a potentially multi-dimensional matrix.

See doc: https://github.com/torch/torch7/blob/master/doc/tensor.md

basic ops

Indicate shape in constructor:

th> x = torch.Tensor(3,4) [0.0000s] th> x 3.7366e+193 …

[OCaml MOOC] week0: intro and overview

Tue, 04 Oct 2016 Category notes OCaml Series Part 1 of «Introduction to Functional Programming in OCaml»

Intro

ML lang family

statically strongly typed languages

fisrt-class functions
type inference
pattern matching

highlights of ocaml

safty: static typing, pattern matching
efficiency: high performance
expressiveness: functional+type inference+polymorphism
package manager: opam
debugger
profiler
REPL: "toplevel"
bytecode compiler: ccamlc
native compiler: ocamlopt

programming environment fully online: https://try.ocamlpro …

[Scala MOOC II] Lec1: For Expressions and Monads

Fri, 12 Aug 2016 Category notes scala Series Part 1 of «Functional Program Design in Scala»

Recap: Functions and Pattern Matching

case classes

ex: json json objects can be seq, num, str, bool,...

⇒ represented as abstract class and case classes.

pattern matching

→ question: what is the type of the {case(key, value)=>"..."} clause?

it is (JBinding => String) type, which is a shorthand for Function1[JBinding, String …

[Scala MOOC I] Lec6: Collections

Sat, 30 Jul 2016 Category notes scala Series Part 7 of «Functional Programming Principles in Scala»

6.1 - Other Collections

so far: only seen List. → more (immutable) collections.

vector

List: is linear -- access to head is faster than middle or end element. Vector: better rand access performance.

represented as very shallow trees(32-split at each node)

Vector support similar operations as List (head, tail,map, fold …

[Scala MOOC I] Lec5: Lists

Fri, 29 Jul 2016 Category notes scala Series Part 6 of «Functional Programming Principles in Scala»

5.1 - More Functions on Lists

already known methods:

xs.head
xs.tail

sublist and ele access:

xs.length
xs.last
xs.init: all elementh except last element
xs.take(n): sublist of first n elements
xs.drop(n): the rest of list after taking first n elements
xs(n …

R语言从入门到放弃 (4). 统计回归

Fri, 29 Jul 2016 Category notes R Series Part 4 of «R语言从入门到放弃»

R里面的统计函数有很多, 这里只用线性模型lm以及(一维)非参估计最常用的三个smoother: Nadaraya-Watson kernel(**NW, **ksmooth), Local Polynomial(**LP, **loess), Smoothing Spline(**SS, **smooth.spline). 用这三个smoother作为例子, 介绍R里面统计回归的一些用法.

数据的形 …