xuanhan863 / practicalAI

A practical approach to learning machine learning.

PracticalAI

Empowering you to use machine learning to get valuable insights from data.

• 🔥 Implement basic ML algorithms and deep neural networks with PyTorch.
• 🖥️ 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	🔥 PyTorch	📚 Advanced RNNs	📸 Computer Vision
🐍 Python	🎛️ Multilayer Perceptrons	🏎️ Highway and Residual Networks	⏰ Time Series Analysis
🔢 NumPy	🔎 Data & Models	🔮 Autoencoders	🏘️ Topic Modeling
🐼 Pandas	📦 Object-Oriented ML	🎭 Generative Adversarial Networks	🛒 Recommendation Systems
📈 Linear Regression	🖼️ Convolutional Neural Networks	🐝 Spatial Transformer Networks	🗣️ Pretrained Language Modeling
📊 Logistic Regression	📝 Embeddings		🤷 Multitask Learning
🌳 Random Forests	📗 Recurrent Neural Networks		🎯 Low Shot Learning
💥 KMeans Clustering			🍒 Reinforcement Learning

Running the notebooks

1. Access the notebooks in the 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 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.

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.

Contributing to notebooks

1. Make your changes and download the Google colab notebook as an .ipynb file.

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

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