这篇文章主要总结了FlutterEngage里的这个talk:
(这个talk的名字的中文翻译挺牛的: "小事一桩：成为开发和设计俱佳的神级人物")
talk里代码地址在: https://github.com/filiph/little_things
这个talk还是挺有意思的, 介绍了如何提升App的设计细节从而获得更高的 "Perceived value …
Dart默认的linter配置有点弱, 有很多有问题代码也不报错或警告. 添加一个analyzer可以应用dart的最佳代码实践, 对一些不好的代码风格提出警告或者直接报错, 从而提高代码质量.
Cf …
我的这个pelican博客已经有八年多了, 从最开始使用pelican2.x+bootstrap2主题到现在使用pelican3, 断断续续经过了各种折腾.
现在的blog虽然文件内容还很乱, 但是网页样式我基本满意. 问题就是pelican3加上我自定义的主题/插件 …
本文总结了Flutter Explained关于fvm的视频: https://youtu.be/R6vKde1vIGQ
background
flutter有很多channel: stable/beta/dev/master, 而目前有些功能只在某个channel上可用, 比如Linux support目前只在dev channel支持.
我日常用的是beta channel, 但是想写Linux程序时每次flutter channel dev …
Overview
Background (Pre-Neural Machine Translation)
- machine translation (MT): sentence from source lang to target lang.
- 1950s: rule based, using bilingual dictionary.
1990s-2010s: Statistical MT (SMT)
using Bayes rule: P(y|x) = P(x|y)*P(y) / P(x)
⇒ The language model we already learnt in prev lectures ⇒ To get the …
Vanishing Gradient Intuition and Proof
ex: grad of loss at position 4 w.r.t.hidden state at postion 1
with chain rule, the grad is smaller as it backprops
⇒
If the largest eigenvalue of Wh is less than 1, the gradient J_i/h_j will exponentially shrink.
Why Vanishing Gradient …
Language Modeling
Language Modeling: task of predicting what words come next.
-
i.e.compute the conditional probability distribution
-
a language model can also be viewed as a system to give probability to a piece of text.
n-gram Language Models
n-gram Language Model: pre-deep learning solution for language modelling.
idea: Collect …
Phrase structure: organize words into nested constituents.
Context-Free Grammars
context-free grammars (CFGs)
- start with words, words are given a category (part of speech = POS):
- words combine into phrases with categories like NP(noun phrase) and PP(prep.phrase):
- Phrases can combine into bigger phrases recursively:
⇒ forms a tree structure:
Dependency …
This week: neural net fundamentals
Classification Setup and Notation
training data:
softmax classifier
(linear classifier — hyperplane):
ith row of the param W: weight vector for class i to compute logits:
prediction = softmax of f_y:
cross-entropy
goal: for (x, y), maximize p(y|x) ⇒ loss for (x, y) = -log p(y …
More Matrix Gradients
⇒
Deriving Gradients wrt Words
pitfall in tetraining word vectors: if some word is not in training data, but other synonyms are present ⇒ only the synonyms word vectors are moved
takeaway:
Backpropagation
backprop:
- apply (generalized) chain rule
- re-use shared stuff
computation graph
⇒ Go backwards along edges, pass along …