1. Basics of Deep Learning No Math Required
Roland Meertens
Machine learning engineer
Autonomous Intelligent Driving

3. What we will learn

4. Inspired by the brain Neurons signal to other neurons
Enough input activation means it becomes activated itself

5. Predicting the price of a house
Area of the house
Age of the house
Distance to train station
Higher activation -> higher price
Weights (influence of that neuron on the output neuron)

6. Predicting the price of a house
Hidden layer
Area of the house
Age of the house
Distance to train station
Too high or too low? Adjust the weights!
Close to the station AND small

7. Activation function
Activation function

9. Representations for characters
Activation per class
(10 output neurons)
Flatten
We could take all 28x28 images, make them into a list of 784 input neurons
Output: an activation per class, 10 output neurons
Probably want even more hidden layers for combinations of combinations of pixels

10. Problems with this approach

11. Create a “feature extractor”
Line
Arc ??
Network will have the chance to learn the same feature at multiple locations

13. Finishing our convolutional network
“Normal” feedforward
Activation per class
Final prediction with a dense layer
Same approach, “this neuron that predicted this feature should have been more active”.

14. What we learned