After using the BN Square layer, you usually need additional layers to achieve the network's goal. Here are some common options:

​Activation Layers: Add non-linearity to the network, enabling it to learn complex patterns. The most famous ones are ReLU and Leaky ReLU.

​Pooling Layers: Reduce the dimensions of the data to retain important features. The most famous one is Max Pooling.

​Dropout Layers: A regularization technique used to prevent the network from overfitting.

​Dense Layers: Connect all the neurons to each other and are used in the final layers for classification or regression.

​In general, the typical sequence of layers after BN Square can be:

​BN Square → ReLU → Pooling → Dropout → Dense#StrategyBTCPurchase #PowellWatch #BinanceHODLerPLUME #ETHInstitutionalFlows #CryptoRally