[Machine Learning for Trading] {ud501} Lesson 25: 03-05 Reinforcement learning | Lesson 26: 03-06 Q-Learning | Lesson 27: 03-07 Dyna

原文地址:https://www.cnblogs.com/ecoflex/p/10977470.html

时间: 2024-08-28 05:49:39

[Machine Learning for Trading] {ud501} Lesson 25: 03-05 Reinforcement learning | Lesson 26: 03-06 Q-Learning | Lesson 27: 03-07 Dyna的相关文章

[Machine Learning for Trading] {ud501} Lesson 3: 01-02 Working with multiple stocks

Lesson outline Lesson outline Here's an overview of what you'll learn to do in this lesson. Documentation links are for reference. Read in multiple stocks: Create an empty pandas.DataFrame with dates as index: pandas.date_range Drop missing date rows

[Machine Learning for Trading] {ud501} Lesson 19: 02-09 The Fundamental Law of active portfolio management | Lesson 20: 02-10 Portfolio optimization and the efficient frontier

this lesson => Buffet said two things => (1) investor skill => (2) breadth / the number of investments Grinold's Fundamental Law breadth => more opportunities to applying that skill => eg. how many stocks you invest in IC => information

[Machine Learning for Trading] {ud501} Lesson 7: 01-06 Histograms and scatter plots

A closer look at daily returns Histogram of daily returns gaussian => kurtosis = 0 How to plot a histogram Computing histogram statistics Select the option that best describes the relationship between XYZ and SPY. Note: These are histograms of daily

[Machine Learning for Trading] {ud501} Lesson 9: 01-08 Optimizers: Building a parameterized model | Lesson 10: 01-09 Optimizers: How to optimize a portfolio

What is an optimizer? Minimization example How to defeat a minimizer Convex problems Building a parameterized model Minimizer finds coefficients What is portfolio optimization? The difference optimization can make Which criteria is easiest to solve f

[Machine Learning for Trading] {ud501} Lesson 21: 03-01 How Machine Learning is used at a hedge fund | Lesson 22: 03-02 Regression

原文地址:https://www.cnblogs.com/ecoflex/p/10977432.html

[Machine Learning for Trading] {ud501} Lesson 23: 03-03 Assessing a learning algorithm | Lesson 24: 03-04 Ensemble learners, bagging and boosting

A closer look at KNN solutions What happens as K varies What happens as D varies Metric 1 RMS Error In Sample vs out of sample Which is worse? Cross validation 5-fold cross validation Roll forward cross validation Metric 2: correlation Correlation an

CS294-112 深度强化学习 秋季学期(伯克利)NO.6 Value functions introduction NO.7 Advanced Q learning

--------------------------------------------------------------------------------------------------------------------------- --------------------------------------------------------------------------------------------------------------------------- un

今天开始学Pattern Recognition and Machine Learning (PRML),章节5.2-5.3,Neural Networks神经网络训练(BP算法)

转载请注明出处:Bin的专栏,http://blog.csdn.net/xbinworld 这一篇是整个第五章的精华了,会重点介绍一下Neural Networks的训练方法--反向传播算法(backpropagation,BP),这个算法提出到现在近30年时间都没什么变化,可谓极其经典.也是deep learning的基石之一.还是老样子,下文基本是阅读笔记(句子翻译+自己理解),把书里的内容梳理一遍,也不为什么目的,记下来以后自己可以翻阅用. 5.2 Network Training 我们可

2020/03/05 生成模型&生成学习(Generative Learning)的流程

在之前的学习2020/01/02 深度学习数学基础学习--朴素贝叶斯中,大概的了解了生成学习的原理,但是对算法实现的 完整流程 不够清晰,所以今天想通过对生成学习回顾,明确一下生成学习的流程框架. 学习资料:斯坦福CS229-note2-Generative Learning algorithms的1.2节 必要的概念 类别先验概率: \(P(c)\) 类条件概率: \(P(\vec x | c)\) ,其中\(\vec x=(x_{1},x_{2},...,x_{m}); m为属性\),\(\