From Neural Networks to Deep Learning

Prof. Dr. Mirco Schoenfeld

Why successful today?

Why is deep learning successful today?

(Raza 2023; Pichler and Hartig 2023)

Moores law

(Roser, Ritchie, and Mathieu 2023)

Moores law

(Roser, Ritchie, and Mathieu 2023)

computational power

  1. Computational power has reached huge capabilities and
    specialized hardware has become available.

data availability

  1. Deep learning handles vast amounts of data efficiently.

end-to-end learning

  1. Characteristic end-to-end learning doesn’t require manual feature engineering.

Transfer learning

  1. Pre-trained state-of-the-art models allow for transfer learning.

DL’s bright future

Deep learning will continue to shape the future of artificial intelligence.

behind the scenes

What’s behind the scenes?

Neural Nets

Deep Learning models are neural networks.

(Shukla 2019)

Neurons

Neural networks are modeled after neural cells.

Artificial Neurons

Artificial Neurons are the elementary
units of artificial neural networks.

Artificial Neurons

An artificial neuron is a function that receives one or more inputs, applies weights to these inputs and sums them to produce an output.

Artificial Neurons

Many artificial neurons together form a neural network.

the input