我是这样度过每一天
----向死而生-----
嘟嘟的安乐死
只记录过程。
与妻谈论给我们生病的兔子嘟嘟实施安乐死，谈论了好几次。
决定的时间也一拖再拖。
她的生命走到了尽头，我们都很清楚。
给了她止疼药，她还能对食物有兴趣，但也吃得越来越少。早已是皮包骨头的她，体重依旧持续下降。
时时翻身摔倒，自己无力起身，徒劳的蹬着腿 。自己的尿常常浸湿身体，把白色的毛染黄，污秽不堪。
并不嫌弃她，我相反觉得很可怜。一次次的扶起她，给她弄吃的。感受她的无助和依旧对生的渴望。
夜晚时，寂静之中常常传来她大声咬牙的声音。我熟悉这因痛苦袭来，而全身紧张而不由自主发出的声音。止疼药对她来说，也失去效力了。
决定不再拖了。
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今天是奇怪的一天
2019 01 26. 早上，被一通猛烈的咳嗽惊醒。是我自己的咳嗽。 终于一口痰从喉头涌起，不吐不快。摸黑起身吐掉它，然后走到了兔子围栏边，拿了...
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ML4T笔记 | 03-04 Ensemble learners, bagging and boosting
01 - Overview
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1988, Michael Kearns and Leslie Valiant: Can a set of weak learners be combined to create a single, strong learner?
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2006, Netflix: $1 million prize for a machine learning algorithm that could do 10% better than their own algorithm at predicting movies people like.
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2009, The winning algorithm: a combination of several algorithm, or an ensemble learners.
Time: 00:00:42
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ML4T笔记 | 03-03 Assessing a learning algorithm
01 - Overview
In this lesson, we’ll learn methods for assessing those algorithms.
Time: 00:00:25
02 - A closer look at KNN solutions
- If we query with X values using 3-NN, for example, as the X moves along the X-axis, the members of the KNN change, thus the resulted Y line (in red) will have small jumps.
- but the beginning and the end of the data, there are parts of the lines are flat (query gives the same value).
- Good thing: it’s not overfitting the data.
- Negative aspect: the model is not able to extrapolate when data is not available.
Time: 00:01:59
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《You Can't Make Me》
人的性格，多种多样，有先天形成，或者后天的原因影响。 做父母的，如果遇到温顺体贴的孩子，大概会少生点儿气（当然并不一定少操心）。但是如果遇到了一...
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折返点-1904---这一周都是夜跑
这一周，除了周日的时候是早上带女儿跑者去超市，其他的时候，都是夜跑。 夜跑的原因，是因为白天实在太懒的原因。 懒，是因为生病的原因...
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请病假的这一天
昨天，听从妻的吩咐，请了一天的病假。 最近状态特别不好。周五开始生病，时而发烧，时而头疼，时而发冷。吃点儿药就退烧。过不多久又烧回来。周一的时候...
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ML4T笔记 | 03-02 Regression
01 - Introduction
supervised regression learning or numerical model:
- using data to build a model that predicts a numerical output based on a set of numerical inputs.
Time: 00:00:23
02 - Parametric regression
Parametric regression is a way of building a model where we represent the model with the number of parameters. Example: build a model that will predict how much it will rain today based on changes in barometric pressure.
- if barometric pressure declines -> bad weather or rain.
- And when barometric pressure increases -> good weather coming.
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新爸五年计划 084 “我生气啦”
每天总有这么一两次，儿子把脸色一沉，嘴里开始念叨“我生气啦”，“我生气啦”。那个“啦”字还要拉长音，再拐个弯儿。
这话，他是从姐姐那里学来的。姐姐是从我和妻那里学来的。
儿子还好办，他多数情况下，并不是遇到了什么真的让他难过生气的事情。如果真的有那样的事情发生，才一岁多的他，自然是咧开嘴大哭着找妈妈。
女儿则不同。她会用这句话来当作自己做错事情的借口。
你为什么打妈妈？- 我生气了！
你为什么大声喊叫？ - 我生气了！
而真实的原因，多数时候是她想要奶吃，而妈妈犹豫了一下，要不要给。或者她要让爸爸陪她玩儿某个游戏，爸爸实在太累了，不愿意动弹。
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ML4T笔记 | 03-01 How Machine Learning is used at a hedge fund
01 - Overview
- The focus is on creating a model that can be used to predict future prices for stocks or other assets.
- machine learning provides a suite of tools that support a data-centric way to build predictive models.
Time: 00:00:29
02 - The ML problem
The problem the machine learning solves.
- building a model.
- A model is something that takes in observations like this x here, run it through some sort of process, and provide a y (prediction).
- X can be multidimensional (e.g. Bollinger Bands, PE ratio, and so on).
- Y is typically single-dimension represents the prediction.