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
- Computational power has reached huge capabilities
and
specialized hardware has become available.
data availability
- Deep learning handles vast amounts of data
efficiently.
end-to-end learning
- Characteristic end-to-end learning doesn’t require manual
feature engineering.
Transfer learning
- Pre-trained state-of-the-art models allow for transfer
learning.
Neural Architecture Search
- Neural Architecture Search proposes optimal model architectures and
hyperparameter tuning automatically.
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.
