Deep Learning

Definition

Deep learning is a subfield of machine learning that uses multi-layered artificial neural networks to learn patterns from large datasets. It excels at tasks like image recognition, speech, and natural language processing.

Purpose

The purpose is to automatically learn features and representations from raw data without heavy manual feature engineering. It enables breakthroughs in AI performance.

Importance

  • Powers state-of-the-art AI in vision, speech, and NLP.
  • Requires large datasets and computing resources.
  • Less interpretable compared to traditional ML methods.
  • Drives both academic research and commercial applications.

How It Works

  1. Define network architecture with multiple hidden layers.
  2. Feed input data and forward propagate through the network.
  3. Compute errors against ground truth.
  4. Backpropagate errors to update weights.
  5. Iterate training until accuracy stabilizes.

Examples (Real World)

  • Google Translate: uses deep neural networks for machine translation.
  • AlphaFold (DeepMind): protein structure prediction with deep learning.
  • Tesla Autopilot: deep neural networks for vision in self-driving.

References / Further Reading

  • Deep Learning — Goodfellow, Bengio & Courville (MIT Press).
  • “ImageNet Classification with Deep CNNs” — Krizhevsky et al., NeurIPS 2012.
  • Stanford CS231n: Convolutional Neural Networks for Visual Recognition.