Deep Learning Architectures

Dive into the world of neural networks and deep learning architectures. From basic perceptrons to complex transformers, understand how these powerful models learn hierarchical representations and solve complex problems.

Interactive Neural Network Visualization

Click neurons to see their function

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Deep Learning Evolution Timeline

1943

McCulloch-Pitts Neuron

First mathematical model of artificial neuron with binary threshold logic

1957

Perceptron

Frank Rosenblatt's perceptron - first trainable neural network with learning algorithm

1986

Backpropagation

Rumelhart, Hinton, and Williams popularized backpropagation for training multi-layer networks

1997

LSTM Networks

Long Short-Term Memory networks solved vanishing gradient problem in RNNs

2012

Deep Learning Breakthrough

AlexNet wins ImageNet, sparking deep learning revolution with CNNs and GPU acceleration

2017

Transformer Architecture

"Attention Is All You Need" introduces transformers, revolutionizing NLP and beyond

Core Deep Learning Architectures

🧠 Artificial Neural Networks (ANN)

The foundation of deep learning - multilayer perceptrons with fully connected layers.

Key Features:
  • Fully connected layers (dense layers)
  • Activation functions (ReLU, sigmoid, tanh)
  • Backpropagation for learning
  • Universal approximation theorem

Architecture: Input → Hidden Layer(s) → Output

Training: Gradient descent with backpropagation

Strengths: Simple, interpretable, good baseline

Weaknesses: Prone to overfitting, struggles with high-dimensional data

Applications:

  • Tabular data prediction
  • Classification tasks
  • Regression problems
  • Function approximation

🖼️ Convolutional Neural Networks (CNN)

Specialized for processing grid-like data such as images, using convolution operations.

Key Components:
  • Convolutional layers with filters/kernels
  • Pooling layers for downsampling
  • Feature maps and hierarchical learning
  • Translation invariance

Architecture: Conv → Pool → Conv → Pool → Flatten → Dense

Key Insight: Local connectivity and parameter sharing

Strengths: Excellent for images, translation invariant

Weaknesses: Large number of parameters, computationally expensive

Famous Models:

  • LeNet-5 (handwritten digits)
  • AlexNet (ImageNet breakthrough)
  • VGG (deep networks)
  • ResNet (skip connections)
  • EfficientNet (compound scaling)

🔄 Recurrent Neural Networks (RNN)

Designed for sequential data with memory through recurrent connections.

Key Features:
  • Hidden state maintains memory
  • Processes sequences step by step
  • Shared parameters across time steps
  • Variable-length input/output

Types: Vanilla RNN, LSTM, GRU, Bidirectional

Problem: Vanishing gradient in long sequences

Solutions: LSTM gates, GRU simplification

Strengths: Handles sequences, maintains context

Applications:

  • Natural language processing
  • Time series prediction
  • Speech recognition
  • Machine translation
  • Sentiment analysis

🎭 Generative Adversarial Networks (GAN)

Two neural networks competing: generator creates fake data, discriminator detects fakes.

Key Components:
  • Generator network (creates fake data)
  • Discriminator network (detects fakes)
  • Adversarial training process
  • Nash equilibrium goal

Training: Min-max game between networks

Loss: Adversarial loss function

Strengths: Generates realistic data, unsupervised

Weaknesses: Training instability, mode collapse

Variants & Applications:

  • DCGAN (Deep Convolutional GAN)
  • StyleGAN (high-quality faces)
  • CycleGAN (image-to-image translation)
  • Pix2Pix (paired image translation)
  • BigGAN (large-scale generation)

🔄 Autoencoders

Unsupervised learning architecture that learns efficient data representations.

Key Components:
  • Encoder (compresses input)
  • Latent space (compressed representation)
  • Decoder (reconstructs input)
  • Reconstruction loss

Types: Vanilla, Denoising, Sparse, Variational (VAE)

Training: Minimize reconstruction error

Strengths: Learns representations, dimensionality reduction

Weaknesses: May lose important information

Applications:

  • Data compression
  • Noise reduction
  • Anomaly detection
  • Feature learning
  • Generative modeling (VAE)

🎯 Attention Mechanisms

Allows models to focus on relevant parts of input, revolutionizing sequence modeling.

Key Concepts:
  • Query, Key, Value matrices
  • Attention weights calculation
  • Dot-product attention
  • Multi-head attention

Formula: Attention(Q,K,V) = softmax(QK^T/√d_k)V

Types: Self-attention, Cross-attention, Multi-head

Strengths: Parallel processing, long-range dependencies

Impact: Foundation of Transformer architecture

Applications:

  • Machine translation
  • Text summarization
  • Image captioning
  • Question answering
  • Transformer models

Interactive Architecture Explorer

Explore Different Architectures

🖼️ CNN Layer-by-Layer Process

1. Input Image: Raw pixel values (e.g., 224x224x3)

2. Convolutional Layer: Apply filters to detect features (edges, shapes)

3. Activation Function: ReLU introduces non-linearity

4. Pooling Layer: Reduce spatial dimensions, retain important features

5. Repeat: Stack conv-pool layers for hierarchy

6. Flatten: Convert 2D feature maps to 1D vector

7. Dense Layers: Final classification or regression

# CNN Architecture Example model = Sequential([ Conv2D(32, (3,3), activation='relu', input_shape=(224,224,3)), MaxPooling2D((2,2)), Conv2D(64, (3,3), activation='relu'), MaxPooling2D((2,2)), Conv2D(128, (3,3), activation='relu'), GlobalAveragePooling2D(), Dense(64, activation='relu'), Dense(10, activation='softmax') ])

Practical Implementation Examples

Code Examples for Different Architectures

🖼️ CNN Implementation - Image Classification

# Complete CNN implementation in TensorFlow/Keras import tensorflow as tf from tensorflow.keras import layers, models import numpy as np # Data preprocessing (x_train, y_train), (x_test, y_test) = tf.keras.datasets.cifar10.load_data() x_train = x_train.astype('float32') / 255.0 x_test = x_test.astype('float32') / 255.0 y_train = tf.keras.utils.to_categorical(y_train, 10) y_test = tf.keras.utils.to_categorical(y_test, 10) # Build CNN model model = models.Sequential([ # First convolutional block layers.Conv2D(32, (3, 3), activation='relu', input_shape=(32, 32, 3)), layers.BatchNormalization(), layers.Conv2D(32, (3, 3), activation='relu'), layers.MaxPooling2D((2, 2)), layers.Dropout(0.25), # Second convolutional block layers.Conv2D(64, (3, 3), activation='relu'), layers.BatchNormalization(), layers.Conv2D(64, (3, 3), activation='relu'), layers.MaxPooling2D((2, 2)), layers.Dropout(0.25), # Third convolutional block layers.Conv2D(128, (3, 3), activation='relu'), layers.BatchNormalization(), layers.Dropout(0.25), # Classification head layers.Flatten(), layers.Dense(512, activation='relu'), layers.BatchNormalization(), layers.Dropout(0.5), layers.Dense(10, activation='softmax') ]) # Compile model model.compile( optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'] ) # Train model history = model.fit( x_train, y_train, batch_size=32, epochs=50, validation_data=(x_test, y_test), callbacks=[ tf.keras.callbacks.EarlyStopping(patience=5), tf.keras.callbacks.ReduceLROnPlateau(factor=0.5, patience=3) ] ) # Evaluate model test_loss, test_acc = model.evaluate(x_test, y_test, verbose=0) print(f'Test accuracy: {test_acc:.4f}')

Key Concepts:

  • Convolutional Layers: Extract spatial features using filters
  • Pooling: Reduce spatial dimensions while preserving features
  • Batch Normalization: Stabilizes training and improves convergence
  • Dropout: Prevents overfitting by randomly disabling neurons
  • Data Augmentation: Increases dataset diversity (rotation, flip, etc.)

Architecture Comparison

Deep Learning Architecture Comparison

Architecture Best For Key Strength Main Weakness Training Difficulty
ANN/MLP Tabular data Simple, interpretable Limited to structured data Easy
CNN Images, spatial data Translation invariance Large parameter count Moderate
RNN/LSTM Sequential data Memory and context Vanishing gradients Challenging
GAN Data generation Realistic synthetic data Training instability Very Challenging
Autoencoder Dimensionality reduction Unsupervised learning Information loss Moderate
Transformer NLP, sequences Parallel processing Computational cost Challenging
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