practicalAI 介绍
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 |
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![](/static/assets/osapp/images/70bb76cbfe2ec5e3138db3bc4488d751.png) [Notebooks](https://colab.research.google.com/github/GokuMohandas/practicalAI/blob/master/notebooks/00_Notebooks.ipynb) | ![](/static/assets/osapp/images/3f7d52722b8696572aba8e51ec74d4ab.png) [PyTorch](https://colab.research.google.com/github/GokuMohandas/practicalAI/blob/master/notebooks/07_PyTorch.ipynb) | ![](/static/assets/osapp/images/645121b8c53d85ea0211ef46e0cb5659.png) [Advanced RNNs](https://colab.research.google.com/github/GokuMohandas/practicalAI/blob/master/notebooks/14_Advanced_RNNs.ipynb) | ![](/static/assets/osapp/images/2a81129b3c5d35e3b0b3e82c8ffb35bd.png) [Computer Vision](https://colab.research.google.com/github/GokuMohandas/practicalAI/blob/master/notebooks/15_Computer_Vision.ipynb) |
![](/static/assets/osapp/images/04815831aa2b1665bd108f53b19d2362.png) [Python](https://colab.research.google.com/github/GokuMohandas/practicalAI/blob/master/notebooks/01_Python.ipynb) | ![](/static/assets/osapp/images/d7f9428bfc1af4b1cead31826004a46c.png) [Multilayer Perceptrons](https://colab.research.google.com/github/GokuMohandas/practicalAI/blob/master/notebooks/08_Multilayer_Perceptron.ipynb) | ![](/static/assets/osapp/images/f394e9e32458fe631a24c94718fdcbd4.png) Highway and Residual Networks | ![](/static/assets/osapp/images/d0074fc67c3a0b070fe9d2dcddb4f622.png) Time Series Analysis |
![](/static/assets/osapp/images/455c12240e2aea8c038eb011ffb697af.png) [NumPy](https://colab.research.google.com/github/GokuMohandas/practicalAI/blob/master/notebooks/02_NumPy.ipynb) | ![](/static/assets/osapp/images/a25b56ce93d6b55dbda3a484cf80e306.png) [Data & Models](https://colab.research.google.com/github/GokuMohandas/practicalAI/blob/master/notebooks/09_Data_and_Models.ipynb) | ![](/static/assets/osapp/images/94b2168c0908c27881ace03a247f10be.png) Autoencoders | ![](/static/assets/osapp/images/1f858bac282cbc6a437e345b9be8283f.png) Topic Modeling |
![](/static/assets/osapp/images/6a2bd16c9036bb6c3085bcbd58087e4e.png) [Pandas](https://colab.research.google.com/github/GokuMohandas/practicalAI/blob/master/notebooks/03_Pandas.ipynb) | ![](/static/assets/osapp/images/51e45ad535dc7b27f7dd256895d80bb6.png) [Object- Oriented ML](https://colab.research.google.com/github/GokuMohandas/practicalAI/blob/master/notebooks/10_Object_Oriented_ML.ipynb) | ![](/static/assets/osapp/images/f18366003edd6451f9d848fa4aaef008.png) Generative Adversarial Networks | ![](/static/assets/osapp/images/f6784b20c66312b9b632bce8a624c99c.png) Recommendation Systems |
![](/static/assets/osapp/images/8dc6745fd63bdea21886f40ac42d7961.png) [Linear Regression](https://colab.research.google.com/github/GokuMohandas/practicalAI/blob/master/notebooks/04_Linear_Regression.ipynb) | ![](/static/assets/osapp/images/af9a1666eca1564cf608aa6dac54df6a.png) [Convolutional Neural Networks](https://colab.research.google.com/github/GokuMohandas/practicalAI/blob/master/notebooks/11_Convolutional_Neural_Networks.ipynb) | ![](/static/assets/osapp/images/255307967e3016c62ede06fb32cb6192.png) Transformer Networks | ![](/static/assets/osapp/images/375668c0e1e468b048392ae3127a4ff7.png) Pretrained Language Modeling |
![](/static/assets/osapp/images/5ed2146acfa5063b3a3a47be71ce44d7.png) [Logistic Regression](https://colab.research.google.com/github/GokuMohandas/practicalAI/blob/master/notebooks/05_Logistic_Regression.ipynb) | ![](/static/assets/osapp/images/7820a6e43f779c5ac47195472d0a4125.png) [Embeddings](https://colab.research.google.com/github/GokuMohandas/practicalAI/blob/master/notebooks/12_Embeddings.ipynb) | Multitask Learning | |
![](/static/assets/osapp/images/7c8a398d199f632a6d3331bc1378a148.png) [Random Forests](https://colab.research.google.com/github/GokuMohandas/practicalAI/blob/master/notebooks/06_Random_Forests.ipynb) | ![](/static/assets/osapp/images/4b167e376152e225aca7f10be0b6128f.png) [Recurrent Neural Networks](https://colab.research.google.com/github/GokuMohandas/practicalAI/blob/master/notebooks/13_Recurrent_Neural_Networks.ipynb) | ![](/static/assets/osapp/images/517d30740f44fcfc9610f368f4000c37.png) One-shot Learning | |
![](/static/assets/osapp/images/605b3b7d8a7ca24eb88e6b9380e1a78d.png) Clustering | ![](/static/assets/osapp/images/38d0e12ce3289e3687c4ad9519c7c503.png) Reinforcement Learning |
运行 notebooks
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在本项目的 notebooks 文件夹,进入 notebooks;
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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;
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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;
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删去标题的 副本 部分,来重命名该 notebook;
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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
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