Code for Machine Learning for Algorithmic Trading, 2nd edition是一本关于机器学习在算法交易中的应用的书籍,主要介绍了如何使用Python和TensorFlow等工具进行机器学习算法的开发和应用。该书的作者是一位知名的机器学习专家,他在书中详细介绍了机器学习算法的基本原理、应用场景和实现方法,同时提供了大量的示例代码和...
Part 2: Machine Learning for Trading: Fundamentals 06 The Machine Learning Process 07 Linear Models: From Risk Factors to Return Forecasts 08 The ML4T Workflow: From Model to Strategy Backtesting 09 Time Series Models for Volatility Forecasts and Statistical Arbitrage ...
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刘建平 博客以及配套代码My blogs and code for machine learning. http://cnblogs.com/pinard展开收起 暂无标签 /lorryZhang/machinelearning MIT 保存更改 取消 发行版 暂无发行版 贡献者(3) 全部 近期动态 接近5年前创建了仓库 不能加载更多了 马建仓 AI 助手 ...
% temp(1) = 0; % because we don't add anything for j = 0 % grad = grad + YOUR_CODE_HERE (using the temp variable) % h=sigmoid(X*theta); for i=1:m, J=J+1/m*(-y(i)*log(h(i))-(1-y(i))*log(1-h(i))); endfor n=length(theta); for i=2:n, J=J+lambda/...
Agentic Reasoning: Reasoning LLMs with Tools for the Deep Research theworldofagents/agentic-reasoning • 7 Feb 2025 We introduce Agentic Reasoning, a framework that enhances large language model (LLM) reasoning by integrating external tool-using agents. Decision Making Language Modeling +3 180 ...
Second, the research team has been investigating whether the accuracy rate can be improved by building their own Machine Learning (ML) models to integrate them later into Dialogflow. The team has been training the models by inputting the dataset of foreign accented Chinese speech samples. For exa...
Whenever you change any code in your machine learning project, you want to verify the code and model quality. During continuous integration, you create and verify assets for your application. As a data scientist, you'll probably focus on creating scripts used for data preparation and model ...
unslothai/unsloth • • 13 Nov 2024 We implement a custom kernel that performs the matrix multiplications and the log-sum-exp reduction over the vocabulary in flash memory, making global memory consumption for the cross-entropy computation negligible.28...
MIT researchers have built a new benchmark tool that can accurately predict how long it takes given code to execute on a computer chip, which can help programmers tweak the code for better performance. Tool predicts how fast code will run on a chip: Machine-learning system should enable devel...