Introduction to Deep Learning

Deep Learning (DL) is a subfield of machine learning (ML) that uses artificial neural networks to model complex patterns in data. Unlike traditional ML, DL automatically learns hierarchical representations, enabling breakthroughs in tasks like image recognition, natural language processing (NLP), and generative AI.

1. Core Neural Network Architectures

Artificial Neural Networks (ANNs)

• What? Mimic the human brain with layers of interconnected "neurons."
• How?
  • Input Layer: Receives raw data (e.g., pixel values in an image).
  • Hidden Layers: Transform data using weights and activation functions (e.g., ReLU).
  • Output Layer: Produces predictions (e.g., "cat" or "dog").
• Example: Predicting house prices based on features like size and location.

Convolutional Neural Networks (CNNs)

• What? Specialized for images and videos.
• Key Idea:
  • Convolution Layers: Detect edges, textures, and patterns using filters (e.g., a filter for vertical lines).
  • Pooling Layers: Reduce image size while preserving important features.
• Example: A CNN recognizes a cat in a photo by first detecting edges, then shapes like ears, and finally the whole cat.
• Models: ResNet (for image classification), YOLO (for object detection).

Recurrent Neural Networks (RNNs)

• What? Designed for sequences (text, speech, time series).
• Key Idea:
  • Hidden State: A "memory" that updates at each step (e.g., remembering the subject of a sentence).
  • Unfolding: Processes data step-by-step (like reading a book page by page).
• Limitation: Struggles with long-term dependencies (e.g., linking "it" to "cat" in a long paragraph).