Neuralnetlib

- fix(LSTM): huge improvements in gradient flow
- feat(gradient_norm): better batch handling
- fix(Callbacks): now fully working generically
- fix(metrics): val_loss
- fix(Embedding): bias in output
- fix(display): val_*
- feat(metric): add pearsonr
- feat(metric): add kurtosis
- feat(metric): add skew
- fix(metric): skewness and kurtosis when variance=0
- fix(gan): batch logs
- fix(activation): config loading
- feat(lstm): improved flexibility and parameterizing
- docs(notebook): fresh run
- ci: bump version to 4.1.0

- fix(LSTM): huge improvements in gradient flow
- feat(gradient_norm): better batch handling
- fix(Callbacks): now fully working generically
- fix(metrics): val_loss
- fix(Embedding): bias in output
- fix(display): val_*
- feat(metric): add pearsonr
- feat(metric): add kurtosis
- feat(metric): add skew
- fix(metric): skewness and kurtosis when variance=0
- fix(gan): batch logs
- fix(activation): config loading
- feat(lstm): improved flexibility and parameterizing
- docs(notebook): fresh run
- ci: bump version to 4.0.7

- refactor: autopep8 format
- ci: bump version to 4.0.6

- fix(cosine_sim): division by zero
- refactor: remove the use of enums
- docs: huge update and new examples
- docs(readme): update hyperlinks
- feat: add SVM
- fix(Dense): bad variable init
- fix(Tokenizer): bpe tokenizing
- fix: jacobian matrix instead of approximation
- fix(AddNorm): backward pass
- ci: bump version to 4.0.4

- feat(optimizers): add AdaBelief
- feat(optimizers): add RAdam
- feat(loss): add focal loss
- fix(optimizers-losses-activations): from_config
- feat(ensemble): add XGBoost
- feat(layer): add ActivationFunction init as string in Activation
- ci: bump version to 4.0.4

- refactor: some improvements
- feat: add validation_split parameter to fit method
- fix(lstm): vanishing and exploding gradient issue
- docs: update readme
- ci: bump version to 4.0.3