How are deep learning model architectures organized?

Deep learning model architectures are organized as a sequence of layers, each performing a specific function in data processing. The main components and principles:

• Input layer receives raw data (e.g., images, text).
• Hidden layers (Dense, Convolutional, Recurrent, etc.) transform data, highlighting features and patterns.
• Output layer produces the final result (classification, regression, etc.).

Types of architectures:

• Fully connected networks (Fully Connected, Dense) — each neuron is connected to all neurons in the previous layer.
• Convolutional Neural Networks (CNNs) — use convolutions to process data with spatial structure (e.g., images).
• Recurrent Neural Networks (RNNs) — process sequential data, considering the context of previous elements.
• Transformers — based on attention mechanisms, effective for processing sequences and context.

An example of a simple architecture in Python using Keras:

from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense

model = Sequential([
    Dense(64, activation='relu', input_shape=(input_dim,)),
    Dense(64, activation='relu'),
    Dense(num_classes, activation='softmax')
])

Thus, the architecture organizes layers and connections between them for effective learning and solving specific tasks.