机器学习基本模型算法介绍(附加案例). Contribute to sd1084741/machine_learning_model development by creating an account on GitHub.
Update GitHub Actions workflows May 10, 2025 package.json Update to eslint 9.27.0 May 17, 2025 package.py Update electron.mjs to desktop.mjs Apr 12, 2025 pyproject.toml Update pyproject.toml Apr 18, 2025 README MIT license Netron is a viewer for neural network, deep learning and machine ...
GitHub资源:https://github.com/josephmisiti/awesome-machine-learning 3、scikit-learn/scikit-learn Introduction scikit-learn is a Python module for machine learning built on top of SciPy and distributed under the 3-Clause BSD license. GitHub资源:https://github.com/scikit-learn/scikit-learn 4、fchol...
Zero-shot transfer learning for dialogue state tracking (DST) enables us tohandle a variety of task-oriented dialogue domains without the expense ofcollecting in-domain data. In this work, we propose to transfer the\textit{cross-task} knowledge from general question answering (QA) corpora forthe...
Samples and Tools for Windows ML. Contribute to microsoft/Windows-Machine-Learning development by creating an account on GitHub.
《Machine Learning Yearning》是吴恩达历时两年,根据自己多年实践经验整理出来的一本机器学习、深度学习实践经验宝典。作为一本 AI 实战圣经,本书主要教你如何在实践中使机器学习算法的实战经验。 Github: https://github.com/deeplearning-ai/machine-learning-yearning-cnlinks.jianshu.com/go?to=https%3A%2F%...
https://github.com/trekhleb/homemade-machine-learning/blob/master/homemade/logistic_regression/logistic_regression.py Demo |逻辑回归-线性边界:基于花瓣长度和花瓣宽度的鸢尾花类预测 https://nbviewer.jupyter.org/github/trekhleb/homemade-machine-learning/blob/master/notebooks/logistic_regression/logistic_re...
github导入代码 原网址 https://github.com/grant81/MinMaxQLearning 代码实现论文 Littman, Michael L. “Markov Games as a Framework for Multi-Agent Reinforcement Learning.” In Machine Learning Proceedings 1994, edited by William W. Cohen and Haym Hirsh, 157–63. San Francisco (CA): Morgan Kaufma...
models various machine learning models To use any of the models please import as follows frommodelsimportSIMPLE,RCL,ResNet,new_resnetmodel=RCL(layers=3,conv_shape=(256,9),timesteps=3,time_conv_shape=(256,9),input_shape=(1024,124),targets=5,optimizer='adam',pool=2,leak=0.3,drop=0.5,ti...
While SHAP can explain the output of any machine learning model, we have developed a high-speed exact algorithm for tree ensemble methods (see ourNature MI paper). Fast C++ implementations are supported forXGBoost,LightGBM,CatBoost,scikit-learnandpysparktree models: ...