Helper notes from Dr. Rowel Atienza's Deep Learning Lessons.
For any questions, please reach us through the following email addresses:
1. Setting up Anaconda on Ubuntu/Linux [PDF]
2. Working with Jupyter Notebook
Notebook is a creative way of presenting your project outputs as it allows users to create documents (notebook) that contains codes, equations, and other visualizations all in one place.
- Activate your working environment
- Install ipykernel:
pip install ipykernel - Adding virtual environment to jupyter:
python -m ipykernel install --user --name=<env_name> - Run notebook:
jupyter notebook
3. Creating your Hugging Face Space [PDF]
4. Using Google Colab
All python scripts should be on your Google Drive where your Notebooks are mounted.
- Using colab to execute a python script: Colab Notebook
- Using colab on your main project: Colab Notebook
Below is a list of experiments / demos regarding specific topics, done on Jupyter Notebook.
| Toolkits | ||
|---|---|---|
| Numpy | Jupyter | |
| Einsum | Jupyter | |
| Einops | Jupyter | |
| Timm | Jupyter | |
| HuggingFace | Model | |
| Dataset | ||
| HF Space/Gradio | Spaces sample | Notes |
| Wandb | Jupyter | |
| Dataloader | Jupyter | |
| Supervised Learning | ||
| Pytorch Lightning | Jupyter | |
| Loss Functions | Jupyter | |
| Object Detection | Jupyter | |
| Online GPUs | ||
| Google Colab | Colab | |
| Kaggle | Jupyter | |
| Building Blocks | ||
| MLP | Jupyter | Notes |
| CNN | Jupyter | Notes |
| RNN/LSTM | Jupyter | |
| Transformers | Soon |
- Stanford's CS231n Computer Vision Basics: YouTube Playlist