Hopefully each of them will get a dedicated blog post on my humble tech blog maczan.pl
- LeNet
- AlexNet - a from scratch guide to build your own AlexNet you can find on my blog: maczan.pl
- VGG 📝 In progress 📚
- ResNet
- Inception
- DenseNet
- RNN
- LSTM
- GRU (Gated Recurrent Units)
- Transformer
- GPT (maybe Karpathy's resources on this topic?)
- GAN
- VAE (Variational autoencoder)
- U-Net
- Siamese Network
- BERT
- Self-attention
- Sequence-to-Sequence
- DQN
- A3C
- Neural ODEs (ordinary differential equations)
- GNN (Graph Neural Network)
- EfficientNet
# clone project
git clone https://github.com/jmaczan/deep-learning-pytorch
cd deep-learning-pytorch
# [OPTIONAL] create conda environment
conda create -n deep-learning-pytorch python=3.9
conda activate deep-learning-pytorch
# install pytorch according to instructions
# https://pytorch.org/get-started/
# install requirements
pip install -r requirements.txt
# clone project
git clone https://github.com/jmaczan/deep-learning-pytorch
cd deep-learning-pytorch
# create conda environment and install dependencies
conda env create -f environment.yaml -n deep-learning-pytorch
# activate conda environment
conda activate deep-learning-pytorch
Train model with default configuration
# train on CPU
python src/train.py trainer=cpu
# train on GPU
python src/train.py trainer=gpu
Train model with chosen experiment configuration from configs/experiment/
python src/train.py experiment=experiment_name.yaml
You can override any parameter from command line like this
python src/train.py trainer.max_epochs=20 data.batch_size=64