Classical Neural Networks (NNs), which form the foundational architecture for a vast array of modern machine learning applications, especially in the domain of classification, are intricate computational structures designed to learn from data. Their architecture invariably begins with an Input Layer, a crucial entry point whose primary function is to receive and represent the raw data. Data encoding for classical NNs involves transforming raw input into a numerical format, typically fixed-size feature vectors, that the network can process. For instance, in an image classification task, encoding might involve flattening a 2D image matrix (e.g., 28x28 pixels) into a 1D vector of 784 pixel intensity values, often normalized to a specific range (e.g., 0 to 1 or -1 to 1) to aid numerical stability and training convergence. For categorical data (e.g., 'red', 'green', 'blue'), encoding schemes like one-hot encoding (creating binary vectors where only one element corresponding to the category is '1' and others are '0') or embedding layers (which learn dense vector representations for categories) are commonly used. Text data might be encoded using techniques like TF-IDF (Term Frequency-Inverse Document Frequency), word embeddings (Word2Vec, GloVe, FastText, which map words to dense vectors capturing semantic relationships), or more advanced contextual embeddings from transformer models like BERT. The core principle of classical NN encoding is to represent diverse data types as numerical vectors that preserve or highlight the information relevant for the classification task. Following the input layer, the core processing power of the NN resides in one or more Hidden Layers, which are the engines of feature learning and transformation within the network. This approach works because each hidden layer systematically transforms its input into a new representation that is, ideally, more conducive to solving the classification problem. Each hidden layer is composed of numerous interconnected processing units called neurons, or nodes, which are fundamental building blocks responsible for performing computations. Within each neuron, a two-step operation occurs: first, the neuron calculates a weighted sum of all the inputs it receives from the neurons in the preceding layer, adding a learnable bias term that allows the neuron to shift its activation function output. These weights associated with each connection are critical parameters that the network learns during training, signifying the importance or strength of that particular input signal to the neuron's computation. The second step within a neuron involves passing this aggregated, weighted sum through a non-linear Activation Function, such as the widely used Rectified Linear Unit (ReLU), the sigmoid function, or the hyperbolic tangent (tanh). The role of these activation functions is paramount; they introduce essential non-linearities into the network's processing, which is what empowers NNs to model and learn complex, non-linear relationships within the data, a capability that distinguishes them from simpler linear models like logistic regression. The introduction of non-linearity is critical because real-world data patterns are rarely linearly separable; these functions allow the network to approximate highly complex decision boundaries. Without these non-linearities, a deep stack of layers would effectively collapse into a single linear transformation, severely limiting the network's expressive power. As data propagates through successive hidden layers, each layer learns to extract increasingly abstract and complex features from the representations generated by the previous layer, creating a hierarchical feature representation.