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experimentdevopsmachine-learningcontrolaideep-learningversion-controlversionk8smachinelearningdeeplearningtrainsexperiment-managermlopstrainsaiclearml UpdatedMay 22, 2025 Python Collection of Open Source Projects Related to GPT,GPT相关开源项目合集🚀、精选🔥🔥 ...
The default container image that's used by GitHub Codespaces includes a set of machine learning libraries that are preinstalled in your codespace. For example, Numpy, pandas, SciPy, Matplotlib, seaborn, scikit-learn, Keras, PyTorch, Requests, and Plotly. For more information about th...
项目地址: https://github.com/trekhleb/homemade-machine-learning
git clone --depth 1 https://github.com/Azure/azureml-examples cd azureml-examples/cli Use --depth 1 to clone only the latest commit to the repository, which reduces the time to complete the operation. The commands in this tutorial are in the files deploy-local-endpoint.sh and deploy-...
git clone --depth 1 https://github.com/Azure/azureml-examples cd azureml-examples/cli Use --depth 1 to clone only the latest commit to the repository, which reduces the time to complete the operation. The commands in this tutorial are in the files deploy-local-endpoint.sh and deploy-...
https://github.com/trekhleb/homemade-machine-learning/blob/master/homemade/linear_regression 代码| 实现示例 https://github.com/trekhleb/homemade-machine-learning/blob/master/homemade/linear_regression/linear_regression.py Demo | 单变量线性回归:用GDP预测城市幸福指数 ...
《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%...
GitHub资源:https://github.com/ZuzooVn/machine-learning-for-software-engineers 6、apache/incubator-predictionio Introduction Apache PredictionIO (incubating) is an open source machine learning framework for developers, data scientists, and end users. It supports event collection, deployment of algorithms...
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, 15