- Debug a deep learning network: A nicely written blog post on how to debug a deep learning model.
- Recipe for training neural networks: An Andrej Karpathy's blog post on how to train neural nets.
- Troubleshooting deep learning models: A great video to watch where many debugging steps are summarized in a comprehensive way.
- Machine learning yearning: A practical manual written by Andrew Ng, which gives a full overview how one should structure a deep learning project.
- Bayesian optimization: Bayesian optimization for hyperparameter search.
- Dive into deep learning: An amazing website with theory of deep learning, code examples, exercises. It starts from basics and covers the most advanced topics in DL.
- Deep learning: The Bible of deep learning written by Ian Goodfellow and Yoshua Bengio and Aaron Courville. If you wanna go deep, this book is a must to read.
- Understanding LSTM networks: A clear written post on LSTMs. Good for a quick overview and recalling some basics.
- Batch normalization explained: A blog post explaining batch normalization in detail.
- See-RNN package: A package that helps to visualize RNNs. Check it out if you want to dig into RNNs.
- Gradient clipping: An article on gradient clipping in RNNs.
- Axiomatic attribution for deep networks: An article about Integrated Gradients, which is a useful tool for debugging neural nets.
- Attribution baselines: A really well written blog post on importance of choosing a good baseline for Integrated Gradients.
- Teachin Large Language Models to Self-Debug: A paper about Rubber Duck self-debugging with LLMs.
- LangChain Docs: LangChain documentation page.
- Clone the repo:
git clone https://github.com/stanislav-chekmenev/debugging-dl-models
- Please, create a new virtual environment with Python=3.10 inside code_debugging directory. Feel free to use any of your choice. I prefer venv.
Conda:
conda create --name <name> python=3.10
conda activate <name>
Virtual environment: For Ubuntu 22.04:
python3 --version # make sure you have python 3.10 installed
sudo apt install python3-venv
python3 -m venv <path/to/venv>
source <path/to/venv>/bin/activate
- Upgrade pip, it might be of an old version
pip install pip --upgrade
- Install requirements:
pip install -r requirements.txt
-
Install VS Code:
- Follow this link for the installation details.
- If you are on Linux Ubuntu starting from 16.04, then please use the following command:
sudo snap install --classic code
-
Create a new project and choose the ealier created virtual env in VS Code:
- Start VS Code and choose the folder for the project where you cloned the repo.