practicalAI - 机器学习教程
MIT
跨平台
Python
软件简介
让你可以使用机器学习从数据中得到有价值见解。
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用 PyTorch 实现基本的机器学习算法和深层神经网络。
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无需任何设置，即可使用 Google Colab 在浏览器上运行。
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学习面向对象的机器学习来编写产品代码，而不仅仅是教程。
Notebooks
| Basics | Deep Learning | Advanced | Topics |
|---|---|---|---|
|  [Notebooks](https://colab.research.google.com/github/GokuMohandas/practicalAI/blob/master/notebooks/00_Notebooks.ipynb) |  [PyTorch](https://colab.research.google.com/github/GokuMohandas/practicalAI/blob/master/notebooks/07_PyTorch.ipynb) |  [Advanced RNNs](https://colab.research.google.com/github/GokuMohandas/practicalAI/blob/master/notebooks/14_Advanced_RNNs.ipynb) |  [Computer Vision](https://colab.research.google.com/github/GokuMohandas/practicalAI/blob/master/notebooks/15_Computer_Vision.ipynb) |
|  [Python](https://colab.research.google.com/github/GokuMohandas/practicalAI/blob/master/notebooks/01_Python.ipynb) |  [Multilayer Perceptrons](https://colab.research.google.com/github/GokuMohandas/practicalAI/blob/master/notebooks/08_Multilayer_Perceptron.ipynb) |  Highway and Residual Networks |  Time Series Analysis |
|  [NumPy](https://colab.research.google.com/github/GokuMohandas/practicalAI/blob/master/notebooks/02_NumPy.ipynb) |  [Data & Models](https://colab.research.google.com/github/GokuMohandas/practicalAI/blob/master/notebooks/09_Data_and_Models.ipynb) |  Autoencoders |  Topic Modeling |
|  [Pandas](https://colab.research.google.com/github/GokuMohandas/practicalAI/blob/master/notebooks/03_Pandas.ipynb) |  [Object- Oriented ML](https://colab.research.google.com/github/GokuMohandas/practicalAI/blob/master/notebooks/10_Object_Oriented_ML.ipynb) |  Generative Adversarial Networks |  Recommendation Systems |
|  [Linear Regression](https://colab.research.google.com/github/GokuMohandas/practicalAI/blob/master/notebooks/04_Linear_Regression.ipynb) |  [Convolutional Neural Networks](https://colab.research.google.com/github/GokuMohandas/practicalAI/blob/master/notebooks/11_Convolutional_Neural_Networks.ipynb) |  Transformer Networks |  Pretrained Language Modeling |
|  [Logistic Regression](https://colab.research.google.com/github/GokuMohandas/practicalAI/blob/master/notebooks/05_Logistic_Regression.ipynb) |  [Embeddings](https://colab.research.google.com/github/GokuMohandas/practicalAI/blob/master/notebooks/12_Embeddings.ipynb) | Multitask Learning | |
|  [Random Forests](https://colab.research.google.com/github/GokuMohandas/practicalAI/blob/master/notebooks/06_Random_Forests.ipynb) |  [Recurrent Neural Networks](https://colab.research.google.com/github/GokuMohandas/practicalAI/blob/master/notebooks/13_Recurrent_Neural_Networks.ipynb) |  One-shot Learning | |
|  Clustering |  Reinforcement Learning |
运行 notebooks
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在本项目的
notebooks文件夹，进入 notebooks； -
你可以在 Google Colab （建议的）或本地机器运行这些 notebook；
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点击一项 notebook，把 notebook 的 URL 替换
https://github.com/成https://colab.research.google.com/github/，或者使用该 Chrome扩展，一键完成操作； -
登入你的 Google 账号；
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点击工具栏上的
复制到云端硬盘按钮，之后会在一个新标签页打开 notebook； -
删去标题的
副本部分，来重命名该 notebook； -
你可以运行代码、做修改等。这将自动存储在你的私人谷歌云盘。