Neural Network Architectures

Dive deep into the foundational building blocks of modern artificial intelligence. This section explores various neural network architectures, their underlying principles, applications, and implementation details.

🧠

Fully Connected Networks (FCNs) / Multi-Layer Perceptrons (MLPs)

The simplest form of neural network, where each neuron in one layer is connected to every neuron in the next layer. Ideal for tabular data and basic classification/regression tasks.

                        # Example: Simple MLP with TensorFlow/Keras
                        from tensorflow.keras.models import Sequential
                        from tensorflow.keras.layers import Dense

                        model = Sequential([
                            Dense(128, activation='relu', input_shape=(784,)),
                            Dense(64, activation='relu'),
                            Dense(10, activation='softmax')
                        ])
                    

🖼️

Convolutional Neural Networks (CNNs)

Dominant in computer vision tasks, CNNs use convolutional layers to automatically learn spatial hierarchies of features from images.

                        # Example: Basic CNN structure
                        from tensorflow.keras.models import Sequential
                        from tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dense

                        model = Sequential([
                            Conv2D(32, (3, 3), activation='relu', input_shape=(28, 28, 1)),
                            MaxPooling2D((2, 2)),
                            Flatten(),
                            Dense(10, activation='softmax')
                        ])
                    

✍️

Recurrent Neural Networks (RNNs)

Designed for sequential data like text and time series, RNNs have loops that allow information to persist, enabling them to process sequences of arbitrary length.

                        # Example: Simple RNN layer
                        from tensorflow.keras.models import Sequential
                        from tensorflow.keras.layers import SimpleRNN, Dense

                        model = Sequential([
                            SimpleRNN(32, activation='relu', input_shape=(None, 10)), # (timesteps, features)
                            Dense(1)
                        ])
                    

🔄

Long Short-Term Memory (LSTM) & Gated Recurrent Unit (GRU)

Advanced variants of RNNs that are better at capturing long-range dependencies and mitigating the vanishing gradient problem, making them highly effective for natural language processing and time series forecasting.

                        # Example: LSTM layer
                        from tensorflow.keras.models import Sequential
                        from tensorflow.keras.layers import LSTM, Dense

                        model = Sequential([
                            LSTM(64, return_sequences=True), # return_sequences=True for stacking
                            LSTM(32),
                            Dense(1)
                        ])
                    

🔗

Transformers

Revolutionary architecture, particularly in NLP, relying on self-attention mechanisms to weigh the importance of different input parts, allowing for parallel processing and capturing complex relationships.

                        # Conceptual representation (Transformers are complex)
                        # Involves Multi-Head Attention, Positional Encoding, Feed-Forward Networks
                        # Libraries like Hugging Face's Transformers provide implementations.
                        print("Transformer architectures are highly complex and typically implemented using specialized libraries.")
                    