Deep Learning

What is Deep Learning?

Deep learning is a type of machine learning that uses multilayered neural networks to perform tasks like classification, regression, and representation learning. The "deep" in deep learning refers to the use of multiple layers in the network—ranging from three to several hundred or thousands.

These networks are designed to process data in ways loosely inspired by biological neurons, stacking artificial neurons into layers and "training" them to recognize patterns. A network is typically called "deep" if it has at least two hidden layers between input and output.

Historical Timeline

1943: Walter Pitts and Warren McCulloch created the first computer model based on neural networks.

1965: Alexey Ivakhnenko published the first working deep learning algorithm (Group Method of Data Handling) in the Soviet Union.

1979: Fukushima introduced early convolutional networks with multiple layers.

1985: Rumelhart, Hinton, and Williams demonstrated that backpropagation could yield useful distributed representations.

1991: Sepp Hochreiter identified the vanishing gradient problem and proposed LSTM (Long Short-Term Memory) with Schmidhuber.

2012: AlexNet's victory in ImageNet revolutionized computer vision and triggered the modern deep learning era.

2017: The Transformer architecture redefined natural language processing.

2022-present: Large language models (GPT, Claude, Gemini) and multimodal models dominate.

Why GPUs Changed Everything

The deep learning revolution came courtesy of the video game industry. The complex imagery and rapid pace of modern games required specialized hardware—graphics processing units (GPUs). Researchers discovered these same chips could accelerate neural network training by orders of magnitude, making deep learning practical.

Common Architectures

• Fully Connected Networks: Every neuron connects to all neurons in adjacent layers
• Convolutional Neural Networks (CNNs): Specialized for image processing
• Recurrent Neural Networks (RNNs): Process sequential data
• Transformers: Attention-based architecture powering modern LLMs
• Generative Adversarial Networks (GANs): Two networks competing to generate realistic outputs

The Three Pioneers

Deep learning's modern success is often attributed to three researchers who persisted through "AI winters" when the approach was unfashionable:

• Geoffrey Hinton - "Godfather of AI," pioneered backpropagation
• Yann LeCun - Invented convolutional networks, now at Meta
• Yoshua Bengio - Advanced recurrent networks, focuses on AI safety

All three received the 2018 Turing Award for their contributions.

Why It Matters

Deep learning transformed AI from rule-based systems to systems that learn from data. Before deep learning, engineers had to manually specify features for recognition tasks. Deep networks learn these features automatically, enabling breakthroughs in:

• Computer vision (image recognition, self-driving cars)
• Natural language processing (translation, chatbots, LLMs)
• Speech recognition (voice assistants)
• Game playing (AlphaGo, chess engines)
• Scientific discovery (protein folding, drug discovery)

Related Reading

• Geoffrey Hinton - Pioneer of backpropagation
• Yann LeCun - Inventor of CNNs
• Yoshua Bengio - RNN and safety researcher