PyTorch Lightning

Define a LightningModule for the CNN architecture.

Implement the training_step to calculate loss and update weights.

Use a LightningDataModule to load and preprocess the images.

Train the model using the Trainer with multiple GPUs for faster training.

Evaluate the model's performance on a validation set.

Define a LightningModule for the transformer architecture.

Implement the training_step to calculate loss and update weights.

Use a LightningDataModule to load and preprocess the text data.

Train the model using the Trainer with mixed precision training for faster convergence.

Evaluate the model's performance on a held-out test set.

Define a LightningModule for the object detection model (e.g., YOLO or Faster R-CNN).

Implement the training_step to calculate loss and update weights.

Use a LightningDataModule to load and preprocess the image/video data.

Train the model using the Trainer with distributed training to scale the training process.

Evaluate the model's performance on a validation set using metrics like mAP.

Define separate LightningModules for the generator and discriminator networks.

Implement the training_step for each network, optimizing them adversarially.

Use a LightningDataModule to load and preprocess the training data.

Train the model using the Trainer, carefully balancing the training of the generator and discriminator.

Evaluate the quality of the generated samples using qualitative and quantitative metrics.

Define a LightningModule for the reinforcement learning agent.

Implement the training_step to update the agent's policy based on the environment feedback.

Use a LightningDataModule to simulate the environment and generate training data.

Train the model using the Trainer, potentially using techniques like experience replay and target networks.

Evaluate the agent's performance by measuring its reward and success rate in the environment.