Master Deep Neural Networks Like Never Before

Key Components of Deep Neural Networks

To effectively master DNNs, it is crucial to understand their key components:

• Input Layer: This layer receives the raw data that the network will process.
• Hidden Layers: These layers perform complex transformations on the input data, extracting features and patterns.
• Output Layer: The final layer produces the network's predictions or classifications.

Each neuron within these layers applies an activation function to its inputs, allowing the network to capture non-linear relationships in the data. Common activation functions include ReLU (Rectified Linear Unit), sigmoid, and tanh.