neuralnetlib 3.3.3

• fix(example): weight init
• docs(examples): fresh run
• docs: update readme
• fix(layers): encoder and decoder layers
• fix(conv2d): align output shape calculation between im2col and convolve
• ci: bump version to 3.3.3

neuralnetlib 3.3.2

• fix(model): save method
• docs: update readme
• docs: update readme
• feat(autoencoder): add variational autoencoder (VAE)
• ci: bump version to 3.3.2

neuralnetlib 3.3.1

• docs: update todo
• feat(preprocessing): add cosine similarity
• docs: update todo
• feat(callbacks): add LearningRateScheduler
• ci: bump version to 3.3.1

neuralnetlib 3.3.0

• docs: update readme
• docs: update readme
• docs: update readme
• docs: remove useless comments
• docs: update readme
• docs: update readme
• refactor: code cleanup and formatting
• fix(config): layers and model config
• fix(metrics): pr auc and roc auc
• refactor(PCA): add explained variance ratio
• feat(Model): add autoencoder model
• feat(preprocessing): add t-SNE
• feat(layers): update compatibility dict
• ci: bump version to 3.3.0

neuralnetlib 3.2.2

• docs: update readme
• refactor: change model -> models for future implementations
• ci: bump version to 3.2.1
• ci: bump version to 3.2.2

neuralnetlib 3.2.1

• docs: update readme
• refactor: change model -> models for future implementations
• ci: bump version to 3.2.1

neuralnetlib 3.2.0

• feat(metrics): add roc auc, pr auc, r2 and classifrep
• feat(model): add gradient clipping and matrix padding optimizations
• docs(notebook): fresh run
• feat(Model): add temperature and sequence generation with improved sampling
• ci: bump version to 3.2.0

neuralnetlib 3.1.2

• feat: add n-gram class
• ci: bump version to 3.1.2

neuralnetlib 3.1.1

• fix: Adam time increment
• ci: bump version to 3.1.1

neuralnetlib 3.1.0

• docs: update dinosaurs example
• perf(Adam/RNN): better gradient handling
• ci: bump version to 3.0.8
• ci: bump version to 3.1.0