practicalAI 机器学习教程

程序名称：practicalAI

授权协议: MIT

操作系统: 跨平台

开发语言: Python

practicalAI 介绍

让你可以使用机器学习从数据中得到有价值见解。

• 用 PyTorch 实现基本的机器学习算法和深层神经网络。

• 无需任何设置，即可使用 Google Colab 在浏览器上运行。

• 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

1. 在本项目的 notebooks 文件夹，进入 notebooks；

2. 你可以在 Google Colab （建议的）或本地机器运行这些 notebook；

3. 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；

4. 登入你的 Google 账号；

5. 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；

6. 删去标题的 副本 部分，来重命名该 notebook；

7. 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。

practicalAI 官网

https://github.com/GokuMohandas/practicalAI