practicalAI cn

AI Practical AI Chinese Version

Give you the ability to use machine learning to gain valuable insights from your data.

• Use PyTorch to implement basic machine learning algorithms and deep neural networks.
• No settings are required, use Google Colab in your browser to run all programs.
• ? Not just a tutorial, but learning product-level object-oriented machine learning programming.

Notebooks

Base	Deep Learning	Advanced	theme
? Notebooks	PyTorch	Advanced RNNs	? Computer Vision Computer Vision
? Python	?️ Multilayer Perceptrons	?️ Highway and Residual Networks	⏰ Time Series Analysis
? NumPy	? Data and Models Data & Models	? Autoencoders	?️ Topic Modeling
? Pandas	? Object-Oriented ML Object-Oriented	Generative Adversarial Networks	? Recommendation Systems
? Linear Regression	?️ Convolutional Neural Networks	? Spatial Transformer Networks	Pretrained Language Modeling
Logistic Regression Logistic Regression	Embeddings		? Multitask Learning
? Random Forests	? Recurrent Neural Networks		Low Shot Learning
? k-means clustering KMeans Clustering			? Reinforcement Learning

View notebooks

If you don't need to run notebooks, you can easily view them using Jupyter nbviewer.

Replace https://github.com/ with https://nbviewer.jupyter.org/github/ , or open https://nbviewer.jupyter.org and enter the URL of the notebook.

Run notebooks

1. Get the notebook in the notebooks folder of this project;
2. You can run these notebooks on Google Colab or local computers;
3. Click on a notebook, and replace https://github.com/ in the URL address to https://colab.research.google.com/github/ , or use this Chrome extension to complete it in one click;
4. Log in to your own Google account;
5. Click复制到云端硬盘on the toolbar and the notebook will open in a new tab;

5. Complete notebook renaming by removing副本in the title;
6. Run code, modify, etc., all of which will be automatically saved to your personal Google Drive.

Contribute notebooks

1. After modification, download the Google Colab notebook as a .ipynb file;

2. Go to https://github.com/LisonEvf/practicalAI-cn/tree/master/notebooks;
3. Click Upload files .

5. Upload this .ipynb file;
6. Write a detailed submission title and description;
7. Name your branch appropriately;
8. Click Propose changes .

Contribution list

Anyone is welcome to participate and improve.

Notebook	Translator
00_Notebooks.ipynb	@amusi
01_Python.ipynb	@amusi
02_NumPy.ipynb	@amusi
03_Pandas.ipynb	@amusi
04_Linear_Regression.ipynb	@jasonhhao
05_Logistic_Regression.ipynb	@jasonhhao
06_Random_Forests.ipynb	@jasonhhao
07_PyTorch.ipynb	@amusi
08_Multilayer_Perceptron.ipynb	@zhyongquan
09_Data_and_Models.ipynb	@zhyongquan
10_Object_Oriented_ML.ipynb	@zhyongquan
11_Convolutional_Neural_Networks.ipynb
12_Embeddings.ipynb	@wengJJ
13_Recurrent_Neural_Networks.ipynb
14_Advanced_RNNs.ipynb
15_Computer_Vision.ipynb