Lesson 6: Deep Learning Architectures

Beyond Basic Neural Networks

While basic neural networks are powerful, specialized architectures have been developed to excel at specific types of tasks. Each architecture leverages unique properties to handle different data types and problems more effectively.

Major Deep Learning Architectures

🖼️ Convolutional Neural Networks (CNNs)

Best for: Image processing, computer vision

Key feature: Convolution layers detect local patterns like edges and textures

Examples: Image classification, object detection, medical imaging

🔄 Recurrent Neural Networks (RNNs)

Best for: Sequential data, time series

Key feature: Memory cells remember previous inputs

Examples: Language translation, speech recognition, stock prediction

🎯 Transformers

Best for: Natural language processing

Key feature: Attention mechanism focuses on relevant parts of input

Examples: GPT, BERT, machine translation, chatbots

Convolutional Neural Networks (CNNs)

CNNs revolutionized computer vision by mimicking how the human visual cortex processes images.

Key Components:

CNN Architecture Layers

Convolutional Layers: Apply filters to detect features like edges, corners, and textures
Pooling Layers: Reduce spatial dimensions while preserving important information
Activation Layers: Add non-linearity (usually ReLU)
Fully Connected Layers: Final classification or regression based on extracted features

How Convolution Works:

A filter (kernel) slides across the image, performing element-wise multiplication and summing the results. This detects specific patterns at different locations.

Recurrent Neural Networks (RNNs)

RNNs can process sequences of varying lengths by maintaining hidden states that act as memory.

RNN Variants:

Long Short-Term Memory (LSTM)

Solves vanishing gradient problem with gates that control information flow

Use case: Long sequences, language modeling

Gated Recurrent Unit (GRU)

Simplified version of LSTM with fewer parameters

Use case: Faster training, smaller datasets

Transformers: The Modern Revolution

Transformers use self-attention mechanisms to process all positions in a sequence simultaneously, leading to better performance and faster training.

Key Innovations:

Self-Attention: Every word can attend to every other word in the sequence
Parallel Processing: Unlike RNNs, transformers process entire sequences at once
Positional Encoding: Adds position information since there's no inherent sequence order

Transformer Success Stories

BERT: Bidirectional encoder for understanding context
GPT: Generative pre-trained transformer for text generation
T5: Text-to-text transfer transformer for various NLP tasks
Vision Transformer (ViT): Applies transformer architecture to images

Specialized Architectures

Generative Adversarial Networks (GANs)

Two networks compete: a generator creates fake data, and a discriminator tries to detect fakes. This competition improves both networks.

Autoencoders

Compress input data into a lower-dimensional representation, then reconstruct it. Useful for dimensionality reduction and anomaly detection.

ResNet (Residual Networks)

Introduces skip connections that allow training very deep networks (100+ layers) by solving vanishing gradient problems.

Choosing the Right Architecture

📸 Computer Vision Tasks

Use CNNs: ResNet, VGG, Inception for image classification

Use R-CNN: For object detection

Use U-Net: For image segmentation

📝 Natural Language Processing

Use Transformers: BERT for understanding, GPT for generation

Use RNNs/LSTMs: For sequential prediction tasks

🎵 Audio/Time Series

Use RNNs/LSTMs: For sequence modeling

Use CNNs: For spectrograms and audio features

Use Transformers: For long-range dependencies

Knowledge Check

Test your understanding of deep learning architectures

1. Which architecture is best suited for image classification tasks?

A) Recurrent Neural Networks (RNN)

B) Convolutional Neural Networks (CNN)

C) Transformers

D) Linear regression

2. What is the main advantage of Transformers over RNNs?

A) They use less memory

B) They can process sequences in parallel

C) They are simpler to implement

D) They require less data

3. What problem do LSTM networks primarily solve?

A) Overfitting

B) Vanishing gradient problem in long sequences

C) Computational complexity

D) Data preprocessing

4. In CNNs, what is the purpose of pooling layers?

A) Add non-linearity

B) Detect features

C) Reduce spatial dimensions while preserving important information

D) Increase the number of parameters

5. What is the key innovation of ResNet architecture?

A) Attention mechanisms

B) Skip connections (residual connections)

C) Convolution operations

D) Recurrent connections

6. Which architecture would you choose for machine translation?

A) CNN only

B) Simple feedforward network

C) Transformer or sequence-to-sequence RNN

D) Decision tree

7. What is the main component that enables Transformers to handle sequences?

A) Convolutional layers

B) Self-attention mechanism

C) Pooling layers

D) Recurrent connections

8. GANs consist of two networks. What are they called?

A) Encoder and decoder

B) Generator and discriminator

C) Input and output networks

D) Forward and backward networks

Quiz Complete!

0/8

Great job! You understand different neural architectures.