PyTorch-Ignite

Load image dataset using `torchvision.datasets`

Define a CNN model using `torch.nn`

Create a training engine using `ignite.engine.Engine`

Attach metrics like accuracy and loss to the engine

Use `ignite.handlers.ModelCheckpoint` for saving model checkpoints

Run the training loop for a specified number of epochs

Load text data using `torchtext`

Define a transformer model using `transformers`

Implement custom data loaders and batching

Create an engine for training

Track metrics like perplexity and BLEU score

Use `ignite.handlers.EarlyStopping` to prevent overfitting

Define generator and discriminator models

Implement separate training loops for generator and discriminator

Use custom event handlers to update models

Monitor metrics like FID score

Log images generated during training to Tensorboard

Define the RL environment using `gym` or similar

Implement the RL agent (e.g., DQN, PPO)

Use an engine to simulate the environment and train the agent

Track metrics like reward and episode length

Use event handlers to update the agent's policy

Load the dataset containing normal and anomalous data

Define an anomaly detection model (e.g., autoencoder)

Train the model to reconstruct normal data

Use an engine to train the model

Evaluate the model using metrics like precision, recall and F1-score

Detect anomalies based on reconstruction error