# practicalAI

**Repository Path**: jarvisAdmin/practicalAI

## Basic Information

- **Project Name**: practicalAI
- **Description**: 📚A practical approach to learning and using machine learning.
- **Primary Language**: Unknown
- **License**: MIT
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2021-12-04
- **Last Updated**: 2021-12-04

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# <img src="https://github.com/GokuMohandas/practicalAI/blob/master/images/logo.png" width="200" />

[![Colab](https://img.shields.io/badge/launch-Colab-orange.svg)](https://github.com/GokuMohandas/practicalAI#notebooks)
[![Binder](https://img.shields.io/badge/launch-Jupyter-45aaf2.svg)](https://mybinder.org/v2/gh/GokuMohandas/practicalAI/master)
[![Kesci](https://img.shields.io/badge/Kesci-中文-cd201f.svg)](https://www.kesci.com/home/column/5c20e4c5916b6200104eea63)
[![MIT](https://img.shields.io/badge/license-MIT-5eba00.svg)](https://github.com/GokuMohandas/practicalAI/blob/master/LICENSE)
[![Twitter](https://img.shields.io/twitter/follow/GokuMohandas.svg?label=Follow&style=social)](https://twitter.com/GokuMohandas)

🎥 - Video lessons coming soon!

Empowering you to use machine learning to get valuable insights from data.
- 🔥 Implement basic ML algorithms and deep neural networks with <a href="https://pytorch.org/" target="_blank" style="color:#ee4c2c">PyTorch</a>.
- 🖥️ Run everything on the browser without any set up using Google Colab.
- 📦 Learn object-oriented ML to code for products, not just tutorials.

## 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|

## Running the notebooks
1. Access the notebooks in the [`notebooks`](https://github.com/GokuMohandas/practicalAI/tree/master/notebooks) directory in this repo.
2. You can run these notebook on Google Colab (recommended) or on your local machine.
3. Click on a notebook and replace `https://github.com/` with `https://colab.research.google.com/github/` in the notebook URL or use this [Chrome extension](https://chrome.google.com/webstore/detail/open-in-colab/iogfkhleblhcpcekbiedikdehleodpjo) to do it with one click.
4. Sign into your Google account.
5. Click the `COPY TO DRIVE` button on the toolbar. This will open the notebook on a new tab.

<img src="https://raw.githubusercontent.com/GokuMohandas/practicalAI/master/images/copy_to_drive.png">

5. Rename this new notebook by removing the `Copy of` part in the title.
6. Run the code, make changes, etc. and it's all automatically saved to you personal Google Drive.

🇨🇳 - If you are from China or another country where Google is blocked, checkout the links above to the other free services like [Jupyter Binder](https://mybinder.org/v2/gh/GokuMohandas/practicalAI/master). Also check out [Kesci](https://www.kesci.com/home/column/5c20e4c5916b6200104eea63) for the content in Chinese.


## Contributing to notebooks
1. Make your changes and download the Google colab notebook as an `.ipynb` file.

<img src="https://raw.githubusercontent.com/GokuMohandas/practicalAI/master/images/download_ipynb.png">

2. Go to https://github.com/GokuMohandas/practicalAI/tree/master/notebooks
3. Click on `Upload files`.

<img src="https://raw.githubusercontent.com/GokuMohandas/practicalAI/master/images/upload.png">

5. Upload the `.ipynb` file.
6. Write a detailed commit title and message.
7. Name your branch appropriately.
8. Click on `Propose changes`.

<img src="https://raw.githubusercontent.com/GokuMohandas/practicalAI/master/images/commit.png">